Methods and systems for content processing

ABSTRACT

Cell phones and other portable devices are equipped with a variety of technologies by which existing functionality can be improved, and new functionality can be provided. Some relate to visual search capabilities, and responding to different image inputs and different contexts. Others relate to processing of image data. Still others concern metadata generation, processing, and representation. Yet others relate to coping with fixed focus limitations of cell phone cameras, e.g., in reading digital watermark data. Still others concern user interface improvements. Yet others concern adapting behavior of a camera-equipped system based on previously-captured imagery. A great number of other features and arrangements are also detailed.

RELATED APPLICATION DATA

This application is a division of application Ser. No. 13/489,090, filedJun. 5, 2012, which is a continuation of application Ser. No.13/197,555, filed Aug. 3, 2011 (now U.S. Pat. No. 8,194,986), which is adivisional of application Ser. No. 12/271,692, filed Nov. 14, 2008 (nowU.S. Pat. No. 8,520,979), which claims priority to provisionalapplications 61/090,083, filed Aug. 19, 2008; 61/096,703, filed Sep. 12,2008; 61/100,643, filed Sep. 26, 2008; 61/103,907, filed Oct. 8, 2008;and 61/110,490, filed Oct. 31, 2008. The disclosures of these patentdocuments are incorporated herein by reference.

BACKGROUND

U.S. Pat. No. 6,947,571 shows a system in which a cell phone cameracaptures content (e.g., image data), and processes same to derive anidentifier. This identifier is submitted to a data structure (e.g., aremote database), which indicates corresponding data or actions. Thecell phone then displays responsive information, or takes responsiveaction. Such sequence of operations is sometimes referred to as “visualsearch.”

Related technologies are shown in patent publications 20070175998,20060240862, 20060056707, 20060026140, 20060012677, 20050227674,20050185060, 20050144455, 20040199387, 20030083098, 20020178410,20020152388, 20020102966, 20020072982, 20010055391, 20010001854, in U.S.Pat. Nos. 7,251,475, 7,174,293, 7,065,559, 7,016,532, 6,993,573,6,941,275, 6,788,293, 6,766,363, 6,675,165, 6,491,217, 6,389,055,6,199,048, 6,121,530, 6,002,946 and 8,565,815.

The presently-detailed technology concerns improvements to suchtechnologies—moving towards the goal of intuitive computing: devicesthat can see and/or hear, and infer the user's desire in that sensedcontext.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an image of a desktop telephone captured by a cell phonecamera.

FIG. 2 shows a collection of similar images found in a repository ofpublic images, by reference to characteristics discerned from the imageof FIG. 1.

FIGS. 3-8A, and 10-14 are flow diagrams detailing methods incorporatingaspects of the present technology.

FIG. 9 is an arty shot of the Eiffel Tower, captured by a cell phoneuser.

FIG. 15 is another image captured by a cell phone user.

FIG. 16 is an image of an underside of a telephone, discovered usingmethods according to aspects of the present technology.

FIGS. 17A and 17B illustrate different linking topologies.

FIG. 18 is an image captured by a cell phone user, depicting anAppalachian Trail trail marker.

FIGS. 19-23 detail methods incorporating aspects of the presenttechnology.

FIG. 24 shows the user interface of one style of cell phone.

FIGS. 25A and 25B illustrate how different dimensions of commonality maybe explored through use of a user interface control of a cell phone.

FIGS. 26A and 26B detail a particular method incorporating aspects ofthe present technology, by which keywords such as Prometheus and PaulManship are automatically determined from a cell phone image.

FIG. 27 shows some of the different data sources that may be consultedin processing imagery according to aspects of the present technology.

FIGS. 28A, 28B and 29 show different processing methods according toaspects of the present technology.

FIG. 30 identifies some of the different processing that may beperformed on image data, in accordance with aspects of the presenttechnology.

FIG. 31 shows an illustrative tree structure that can be employed inaccordance with certain aspects of the present technology.

FIG. 32 shows a network of wearable computers (e.g., cell phones) thatcan cooperate with each other, e.g., in a peer-to-peer network.

FIGS. 33-35 detail how a glossary of signs can be identified by a cellphone, and used to trigger different actions.

FIG. 36 illustrates aspects of prior art digital camera technology.

FIG. 37 details an embodiment incorporating aspects of the presenttechnology.

FIG. 38 shows how a cell phone can be used to sense and display affineparameters.

FIG. 39 illustrates certain state machine aspects of the presenttechnology.

FIG. 40 illustrates how even “still” imagery can include temporal, ormotion, aspects.

FIG. 41 shows some metadata that may be involved in an implementationincorporating aspects of the present technology.

FIG. 42 shows an image captured by a cell phone camera user.

FIGS. 43-46 detail how the image of FIG. 42 can be processed to conveysemantic metadata.

FIG. 47 shows another image that may be captured by a cell phone camerauser.

FIGS. 48 and 49 detail how the image of FIG. 47 can be processed toconvey semantic metadata.

FIG. 50 shows an image that may be captured by a cell phone camera user.

FIG. 51 details how the image of FIG. 50 can be processed to conveysemantic metadata.

FIG. 52 is a chart showing aspects of the human visual system.

FIG. 53 shows different low, mid and high frequency components of animage.

FIG. 54 shows a newspaper page.

FIG. 55 shows the layout of the FIG. 54 page, as set by layout software.

FIG. 56 details how user interaction with imagery captured from printedtext may be enhanced.

FIGS. 56A and 56B illustrate how semantic conveyance of metadata canhave a progressive aspect, akin to JPEG2000 and the like.

DISCUSSION

Collections of publicly-available imagery and other content are becomingmore prevalent. Flickr, YouTube, Photobucket (MySpace), Picasa, Zooomr,FaceBook, Webshots and Google Images are just a few. Often, theseresources can also serve as sources of metadata—either expresslyidentified as such, or inferred from data such as file names,descriptions, etc. Sometimes geo-location data is also available.

An illustrative embodiment according to one aspect of the presenttechnology works as follows. A user takes a cell phone picture of anobject, or scene—perhaps a desk telephone, as shown in FIG. 1. (Theimage may be acquired in other manners as well, such as transmitted fromanother user.)

As a preliminary operation, known image processing operations may beapplied, e.g., to correct color or contrast, to performortho-normalization, etc. Known image object segmentation orclassification techniques may also be used to identify an apparentsubject region of the image, and isolate same for further processing.

The image data is then processed to determine characterizing featuresthat are useful in pattern matching and recognition. Color, shape, andtexture metrics are commonly used for this purpose. Images may also begrouped based on layout and eigenvectors (the latter being particularlypopular for facial recognition). Many other technologies can of coursebe employed, as noted below.

(Uses of vector characterizations/classifications and otherimage/video/audio metrics in recognizing faces, imagery, video, audioand other patterns are well known and suited for use in connection withthe present technology. See, e.g., patent publications 20070239756,20070237364, 20070058865, 20060020630, 20050267817, 20050180635,20040243567, 20030126121, 20030059124, 20020037083, U.S. Pat. Nos.7,359,889, 7,194,752, 7,174,293, 7,130,466, 6,990,453, 6,681,032,6,553,136, 6,430,307, 6,430,306, 6,072,894, and 4,776,017, and thejournal references cited at the end of this disclosure. When used inconjunction with recognition of entertainment content such as audio andvideo, such features are sometimes termed content “fingerprints” or“hashes.”)

After feature metrics for the cell phone image are determined, a searchis conducted through one or more publicly-accessible image repositoriesfor images with similar metrics, thereby identifying apparently similarimages. (As part of its image ingest process, Flickr and other suchrepositories may calculate eigenvectors or other classification data onimages at the time they are uploaded by users, and collect same in anindex for public search.) The search may yield the collection ofapparently similar telephone images found in Flickr, depicted in FIG. 2.

Metadata is then harvested from Flickr for each of these images, and thedescriptive terms are parsed and ranked by frequency of occurrence. Inthe depicted set of images, for example, the descriptors harvested fromsuch operation, and their incidence of occurrence, may be as follows:

Cisco (18)

Phone (10)

Telephone (7)

VOIP (7)

IP (5)

7941 (3)

Phones (3)

Technology (3)

7960 (2)

7920 (1)

7950 (1)

Best Buy (1)

Desk (1)

Ethernet (1)

IP-phone (1)

Office (1)

Pricey (1)

Sprint (1)

Telecommunications (1)

Uninett (1)

Work (1)

From this aggregated set of inferred metadata, it may be assumed thatthose terms with the highest count values (e.g., those terms occurringmost frequently) are the terms that most accurately characterize theuser's FIG. 1 image.

The inferred metadata can be augmented or enhanced, if desired, by knownimage recognition/classification techniques. Such technology seeks toprovide automatic recognition of objects depicted in images. Forexample, by recognizing a TouchTone keypad layout, and a coiled cord,such a classifier may label the FIG. 1 image using the terms Telephoneand Facsimile Machine.

If not already present in the inferred metadata, the terms returned bythe image classifier can be added to the list and given a count value.(An arbitrary value, e.g., 2, may be used, or a value dependent on theclassifier's reported confidence in the discerned identification can beemployed.)

If the classifier yields one or more terms that are already present, theposition of the term(s) in the list may be elevated. One way to elevatea term's position is by increasing its count value by a percentage(e.g., 30%). Another way is to increase its count value to one greaterthan the next-above term that is not discerned by the image classifier.(Since the classifier returned the term “Telephone” but not the term“Cisco,” this latter approach could rank the term Telephone with a countvalue of “19”—one above Cisco.) A variety of other techniques foraugmenting/enhancing the inferred metadata with that resulting from theimage classifier are straightforward to implement.

A revised listing of metadata, resulting from the foregoing, may be asfollows:

Telephone (19)

Cisco (18)

Phone (10)

VOIP (7)

IP (5)

7941 (3)

Phones (3)

Technology (3)

7960 (2)

Facsimile Machine (2)

7920 (1)

7950 (1)

Best Buy (1)

Desk (1)

Ethernet (1)

IP-phone (1)

Office (1)

Pricey (1)

Sprint (1)

Telecommunications (1)

Uninett (1)

Work (1)

The list of inferred metadata can be restricted to those terms that havethe highest apparent reliability, e.g., count values. A subset of thelist comprising, e.g., the top N terms, or the terms in the top Mthpercentile of the ranked listing, may be used. This subset can beassociated with the FIG. 1 image in a metadata repository for thatimage, as inferred metadata.

In the present example, if N=4, the terms Telephone, Cisco, Phone andVOIP are associated with the FIG. 1 image.

Once a list of metadata is assembled for the FIG. 1 image (by theforegoing procedure, or others), a variety of operations can beundertaken.

One option is to submit the metadata, along with the captured content ordata derived from the captured content (e.g., the FIG. 1 image, imagefeature data such as eigenvalues, machine readable data decoded from theimage, etc), to a service provider that acts on the submitted data, andprovides a response to the user. Shazam, Snapnow, ClusterMedia Labs,Snaptell, Mobot, Mobile Acuity and Digimarc Mobile, are a few of severalcommercially available services that capture media content, and providea corresponding response; others are detailed in the earlier-citedpatent publications. By accompanying the content data with the metadata,the service provider can make a more informed judgment as to how itshould respond to the user's submission.

The service provider—or the user's device—can submit the metadatadescriptors to one or more other services, e.g., a web search enginesuch as Google, to obtain a richer set of auxiliary information that mayhelp better discern/infer/intuit an appropriate desired by the user. Orthe information obtained from Google (or other such database resource)can be used to augment/refine the response delivered by the serviceprovider to the user. (In some cases, the metadata—possibly accompaniedby the auxiliary information received from Google—can allow the serviceprovider to produce an appropriate response to the user, without evenrequiring the image data.)

In some cases, one or more images obtained from Flickr may besubstituted for the user's image. This may be done, for example, if aFlickr image appears to be of higher quality (using sharpness,illumination histogram, or other measures), and if the image metrics aresufficiently similar. (Similarity can be judged by a distance measureappropriate to the metrics being used. One embodiment checks whether thedistance measure is below a threshold. If several alternate images passthis screen, then the closest image is used.) Or substitution may beused in other circumstances. The substituted image can then be usedinstead of (or in addition to) the cell phone image in the arrangementsdetailed herein.

In one such arrangement, the substitute image data is submitted to theservice provider. In another, data for several substitute images aresubmitted. In another, the original image data—together with one or morealternative sets of image data—are submitted. In the latter two cases,the service provider can use the redundancy to help reduce the chance oferror—assuring an appropriate response is provided to the user. (Or theservice provider can treat each submitted set of image dataindividually, and provide plural responses to the user. The clientsoftware on the cell phone can then assess the different responses, andpick between them (e.g., by a voting arrangement), or combine theresponses, to help provide the user an enhanced response.))

Instead of substitution, one or more closely-matching public image(s)may be composited or merged with the user's cell phone image. Theresulting hybrid image can then be used in the different contextsdetailed in this disclosure.

A still further option is to use apparently-similar images gleaned fromFlickr to inform enhancement of the user's image. Examples include colorcorrection/matching, contrast correction, glare reduction, removingforeground/background objects, etc. By such arrangement, for example,such a system may discern that the FIG. 1 image has foregroundcomponents (apparently Post-It notes) on the telephone that should bemasked or disregarded. The user's image data can be enhancedaccordingly, and the enhanced image data used thereafter.

Relatedly, the user's image may suffer some impediment, e.g., such asdepicting its subject from an odd perspective, or with poor lighting,etc. This impediment may cause the user's image not to be recognized bythe service provider (i.e., the image data submitted by the user doesnot seem to match any image data in the database being searched). Eitherin response to such a failure, or proactively, data from similar imagesidentified from Flickr may be submitted to the service provider asalternatives—hoping they might work better.

Another approach—one that opens up many further possibilities—is tosearch Flickr for one or more images with similar image metrics, andcollect metadata as described herein (e.g., Telephone, Cisco, Phone,VOIP). Flickr is then searched a second time, based on metadata. Pluralimages with similar metadata can thereby be identified. Data for thesefurther images (including images with a variety of differentperspectives, different lighting, etc.) can then be submitted to theservice provider—notwithstanding that they may “look” different than theuser's cell phone image.

When doing metadata-based searches, identity of metadata may not berequired. For example, in the second search of Flickr just-referenced,four terms of metadata may have been associated with the user's image:Telephone, Cisco, Phone and VOIP. A match may be regarded as an instancein which a subset (e.g., three) of these terms is found.

Another approach is to rank matches based on the rankings of sharedmetadata terms. An image tagged with Telephone and Cisco would thus beranked as a better match than an image tagged with Phone and VOIP. Oneadaptive way to rank a “match” is to sum the counts for the metadatadescriptors for the user's image (e.g., 19+18+10+7=54), and then tallythe count values for shared terms in a Flickr image (e.g., 35, if theFlickr image is tagged with Cisco, Phone and VOIP). The ratio can thenbe computed (35/54) and compared to a threshold (e.g., 60%). In thiscase, a “match” is found. A variety of other adaptive matchingtechniques can be devised by the artisan based on the foregoing.

The above examples searched Flickr for images based on similarity ofimage metrics, and optionally on similarity of textual (semantic)metadata. Geolocation data (e.g., GPS tags) can also be used to get ametadata toe-hold.

If the user captures an arty, abstract shot of the Eiffel tower fromamid the metalwork or another unusual vantage point (e.g., FIG. 9), itmay not be recognized—from image metrics—as the Eiffel tower. But GPSinfo captured with the image identifies the location of the imagesubject. Public databases (including Flickr) can be employed to retrievetextual metadata based on GPS descriptors. Inputting GPS descriptors forthe photograph yields the textual descriptors Paris and Eiffel.

Google Images, or another database, can be queried with the terms Eiffeland Paris to retrieve other, more perhaps conventional images of theEiffel tower. One or more of those images can be submitted to theservice provider to drive its process. (Alternatively, the GPSinformation from the user's image can be used to search Flickr forimages from the same location; yielding imagery of the Eiffel Tower thatcan be submitted to the service provider.)

Although GPS is gaining in camera-metadata-deployment, most imagerypresently in Flickr and other public databases is missing geolocationinfo. But GPS info can be automatically propagated across a collectionof imagery that share visible features (by image metrics such aseigenvalues, or other classification techniques), or that have ametadata match.

To illustrate, if the user takes a cell phone picture of a cityfountain, and the image is tagged with GPS information, it can besubmitted to a process that identifies matching Flickr/Google images ofthat fountain on a feature-recognition basis. To each of those imagesthe process can add GPS information from the user's image.

A second level of searching can also be employed. From the set offountain images identified from the first search based on similarity ofappearance, metadata can be harvested and ranked, as above. Flickr canthen be searched a second time, for images having metadata that matcheswithin a specified threshold (e.g., as reviewed above). To those images,too, GPS information from the user's image can be added.

Alternatively, or in addition, a first set of images in Flickr/Googlesimilar to the user's image of the fountain can be identified—not bypattern matching, but by GPS-matching (or both). Metadata can beharvested and ranked from these GPS-matched images. Flickr can besearched a second time for a second set of images with similar metadata.To this second set of images, GPS information from the user's image canbe added.

Another approach to geolocating imagery is by searching Flickr forimages having similar image characteristics (gist, color histogram,etc.), and assessing geolocation data in the identified images to inferthe probable location of the original image. See, e.g., Hays, et al,IM2GPS: Estimating geographic information from a single image, Proc. ofthe IEEE Conf. on Computer Vision and Pattern Recognition, 2008.Techniques detailed in the Hays paper are suited for use in conjunctionwith the present technology (including use of probability functions asquantizing the uncertainty of inferential techniques).

When geolocation data is captured by the camera, it is highly reliable.Also generally reliable is metadata (location or otherwise) that isauthored by the proprietor of the image. However, when metadatadescriptors (geolocation or semantic) are inferred or estimated, orauthored by a stranger to the image, uncertainty and other issues arise.

Desirably, such intrinsic uncertainty should be memorialized in somefashion so that later users thereof (human or machine) can take thisuncertainty into account.

One approach is to segregate uncertain metadata from device-authored orcreator-authored metadata. For example, different data structures can beused. Or different tags can be used to distinguish such classes ofinformation. Or each metadata descriptor can have its own sub-metadata,indicating the author, creation date, and source of the data. The authoror source field of the sub-metadata may have a data string indicatingthat the descriptor was inferred, estimated, deduced, etc., or suchinformation may be a separate sub-metadata tag.

Each uncertain descriptor may be given a confidence metric or rank. Thisdata may be determined by the public, either expressly or inferentially.An example is the case when a user sees a Flickr picture she believes tobe from Yellowstone, and adds a “Yellowstone” location tag, togetherwith a “95%” confidence tag (her estimation of certainty about thecontributed location metadata). She may add an alternate locationmetatag, indicating “Montana,” together with a corresponding 50%confidence tag. (The confidence tags needn't sum to 100%. Just one tagcan be contributed—with a confidence less than 100%. Or several tags canbe contributed—possibly overlapping, as in the case with Yellowstone andMontana).

If several users contribute metadata of the same type to an image (e.g.,location metadata), the combined contributions can be assessed togenerate aggregate information. Such information may indicate, forexample, that 5 of 6 users who contributed metadata tagged the image asYellowstone, with an average 93% confidence; that 1 of 6 users taggedthe image as Montana, with a 50% confidence, and 2 of 6 users tagged theimage as Glacier National park, with a 15% confidence, etc.

Inferential determination of metadata reliability can be performed,either when express estimates made by contributors are not available, orroutinely. An example of this is the FIG. 1 photo case, in whichmetadata occurrence counts are used to judge the relative merit of eachitem of metadata (e.g., Telephone=19 or 7, depending on the methodologyused). Similar methods can be used to rank reliability when severalmetadata contributors offer descriptors for a given image.

Crowd-sourcing techniques are known to parcel image-identification tasksto online workers, and collect the results. However, prior artarrangements are understood to seek simple, short-term consensus onidentification. Better, it seems, is to quantify the diversity ofopinion collected about image contents (and optionally its variationover time, and information about the sources relied-on), and use thatricher data to enable automated systems to make more nuanced decisionsabout imagery, its value, its relevance, its use, etc.

To illustrate, known crowd-sourcing image identification techniques mayidentify the FIG. 15 image with the identifiers “soccer ball” and “dog.”These are the consensus terms from one or several viewers. Disregarded,however, may be information about the long tail of alternativedescriptors, e.g., summer, Labrador, football, tongue, afternoon,evening, morning, fescue, etc. Also disregarded may be demographic andother information about the persons (or processes) that served asmetadata identifiers, or the circumstances of their assessments. Aricher set of metadata may associate with each descriptor a set ofsub-metadata detailing this further information.

The sub-metadata may indicate, for example, that the tag “football” wascontributed by a 21 year old male in Brazil on Jun. 18, 2008. It mayfurther indicate that the tags “afternoon,” “evening” and “morning” werecontributed by an automated image classifier at the University of Texasthat made these judgments on Jul. 2, 2008 based, e.g., on the angle ofillumination on the subjects. Those three descriptors may also haveassociated probabilities assigned by the classifier, e.g., 50% forafternoon, 30% for evening, and 20% for morning (each of thesepercentages may be stored as a sub-metatag). One or more of the metadataterms contributed by the classifier may have a further sub-tag pointingto an on-line glossary that aids in understanding the assigned terms.For example, such as sub-tag may give the URL of a computer resourcethat associates the term “afternoon” with a definition, or synonyms,indicating that the term means noon to 7 pm. The glossary may furtherindicate a probability density function, indicating that the mean timemeant by “afternoon” is 3:30 pm, the median time is 4:15 pm, and theterm has a Gaussian function of meaning spanning the noon to 7 pm timeinterval.

Expertise of the metadata contributors may also be reflected insub-metadata. The term “fescue” may have sub-metadata indicating it wascontributed by a 45 year old grass seed farmer in Oregon. An automatedsystem can conclude that this metadata term was contributed by a personhaving unusual expertise in a relevant knowledge domain, and maytherefore treat the descriptor as highly reliable (albeit maybe nothighly relevant). This reliability determination can be added to themetadata collection, so that other reviewers of the metadata can benefitfrom the automated system's assessment.

Assessment of the contributor's expertise can also be self-made by thecontributor. Or it can be made otherwise, e.g., by reputational rankingsusing collected third party assessments of the contributor's metadatacontributions. (Such reputational rankings are known, e.g., from publicassessments of sellers on EBay, and of book reviewers on Amazon.)Assessments may be field-specific, so a person may be judged (orself-judged) to be knowledgeable about grass types, but not about dogbreeds. Again, all such information is desirably memorialized insub-metatags (including sub-sub-metatags, when the information is abouta sub-metatag).

More information about crowd-sourcing, including use of contributorexpertise, etc., is found in published patent application 20070162761.

Returning to the case of geolocation descriptors (which may be numeric,e.g., latitude/longitude, or textual), an image may accumulate—overtime—a lengthy catalog of contributed geographic descriptors. Anautomated system (e.g., a server at Flickr) may periodically review thecontributed geotag information, and distill it to facilitate public use.For numeric information, the process can apply known clusteringalgorithms to identify clusters of similar coordinates, and average sameto generate a mean location for each cluster. For example, a photo of ageyser may be tagged by some people with latitude/longitude coordinatesin Yellowstone, and by others with latitude/longitude coordinates ofHells Gate Park in New Zealand. These coordinates thus form distinct twoclusters that would be separately averaged. If 70% of the contributorsplaced the coordinates in Yellowstone, the distilled (averaged) valuemay be given a confidence of 70%. Outlier data can be maintained, butgiven a low probability commensurate with its outlier status. Suchdistillation of the data by a proprietor can be stored in metadatafields that are readable by the public, but not writable.

The same or other approach can be used with added textual metadata—e.g.,it can be accumulated and ranked based on frequency of occurrence, togive a sense of relative confidence.

The technology detailed in this specification finds numerousapplications in contexts involving watermarking, bar-coding,fingerprinting, OCR-decoding, and other approaches for obtaininginformation from imagery. Consider again the FIG. 1 cell phone photo ofa desk phone. Flickr can be searched based on image metrics to obtain acollection of subject-similar images (e.g., as detailed above). A dataextraction process (e.g., watermark decoding, fingerprint calculation,barcode- or OCR-reading) can be applied to some or all of the resultingimages, and information gleaned thereby can be added to the metadata forthe FIG. 1 image, and/or submitted to a service provider with image data(either for the FIG. 1 image, and/or for related images).

From the collection of images found in the first search, text or GPSmetadata can be harvested, and a second search can be conducted forsimilarly-tagged images. From the text tags Cisco and VOIP, for example,a search of Flickr may find a photo of the underside of the user'sphone—with OCR-readable data—as shown in FIG. 16. Again, the extractedinformation can be added to the metadata for the FIG. 1 image, and/orsubmitted to a service provider to enhance the response it is able toprovide to the user.

As just shown, a cell phone user may be given the ability to look aroundcorners and under objects—by using one image as a portal to a largecollection of related images.

User Interface

Referring to FIGS. 24 and 25A, cell phones and related portable devices10 typically include a display 11 and a keypad 12. In addition to anumeric (or alphanumeric) keypad there is often a multi-functioncontroller 14. One popular controller has a center button 18, and foursurrounding buttons 16 a, 16 b, 16 c and 16 d.

An illustrative usage model is as follows. A system responds to an image28 (either optically captured or wirelessly received) by displaying acollection of related images to the user, on the cell phone display. Forexample, the user captures an image and submits it to a remote service.The service determines image metrics for the submitted image (possiblyafter pre-processing, as detailed above), and searches (e.g., Flickr)for visually similar images. These images are transmitted to the cellphone (e.g., by the service, or directly from Flickr), and they arebuffered for display. The service can prompt the user, e.g., byinstructions presented on the display, to repeatedly press theright-arrow button 16 b on the four-way controller (or press-and-hold)to view a sequence of pattern-similar images (30, FIG. 25A). Each timethe button is pressed, another one of the buffered apparently-similarimages is displayed.

By techniques like those earlier described, or otherwise, the remoteservice can also search for images that are similar in geolocation tothe submitted image. These too can be sent to and buffered at the cellphone. The instructions may advise that the user can press theleft-arrow button 16 d of the controller to review these GPS-similarimages (32, FIG. 25A).

Similarly, the service can search for images that are similar inmetadata to the submitted image (e.g., based on textual metadatainferred from other images, identified by pattern matching or GPSmatching). Again, these images can be sent to the phone and buffered forimmediate display. The instructions may advise that the user can pressthe up-arrow button 16 a of the controller to view thesemetadata-similar images (34, FIG. 25A).

Thus, by pressing the right, left, and up buttons, the user can reviewimages that are similar to the captured image in appearance, location,or metadata descriptors.

Whenever such review reveals a picture of particular interest, the usercan press the down button 16 c. This action identifies thecurrently-viewed picture to the service provider, which then can repeatthe process with the currently-viewed picture as the base image. Theprocess then repeats with the user-selected image as the base, and withbutton presses enabling review of images that are similar to that baseimage in appearance (16 b), location (16 d), or metadata (16 a).

This process can continue indefinitely. At some point the user can pressthe center button 18 of the four-way controller. This action submits thethen-displayed image to a service provider for further action (e.g.,triggering a corresponding response, as disclosed, e.g., inearlier-cited documents). This action may involve a different serviceprovider than the one that provided all the alternative imagery, or theycan be the same. (In the latter case the finally-selected image need notbe sent to the service provider, since that service provider knows allthe images buffered by the cell phone, and may track which image iscurrently being displayed.)

The dimensions of information browsing just-detailed (similar-appearanceimages; similar-location images; similar-metadata images) can bedifferent in other embodiments. Consider, for example, an embodimentthat takes an image of a house as input (or latitude/longitude), andreturns the following sequences of images: (a) the houses for salenearest in location to the input-imaged house; (b) the houses for salenearest in price to the input-imaged house; and (c) the houses for salenearest in features (e.g., bedrooms/baths) to the input-imaged house.(The universe of houses displayed can be constrained, e.g., by zip-code,metropolitan area, school district, or other qualifier.)

Another example of this user interface technique is presentation ofsearch results from EBay for auctions listing Xbox 360 game consoles.One dimension can be price (e.g., pushing button 16 b yields a sequenceof screens showing Xbox 360 auctions, starting with the lowest-pricedones); another can be seller's geographical proximity to user (closestto furthest, shown by pushing button 16 d); another can be time untilend of auction (shortest to longest, presented by pushing button 16 a).Pressing the middle button 18 can load the full web page of the auctionbeing displayed.

A related example is a system that responds to a user-captured image ofa car by identifying the car (using image features and associateddatabase(s)), searching EBay and Craigslist for similar cars, andpresenting the results on the screen. Pressing button 16 b presentsscreens of information about cars offered for sale (e.g., includingimage, seller location, and price) based on similarity to the inputimage (same model year/same color first, and then nearest modelyears/colors), nationwide. Pressing button 16 d yields such a sequenceof screens, but limited to the user's state (or metropolitan region, ora 50 mile radius of the user's location, etc). Pressing button 16 ayields such a sequence of screens, again limited geographically, butthis time presented in order of ascending price (rather than closestmodel year/color). Again, pressing the middle button loads the full webpage (EBay or Craigslist) of the car last-displayed.

Another embodiment is an application that helps people recall names. Auser sees a familiar person at a party, but can't remember his name.Surreptitiously the user snaps a picture of the person, and the image isforwarded to a remote service provider. The service provider extractsfacial recognition parameters and searches social networking sites(e.g., FaceBook, MySpace, Linked-In), or a separate database containingfacial recognition parameters for images on those sites, forsimilar-appearing faces. (The service may provide the user's sign-oncredentials to the sites, allowing searching of information that is nototherwise publicly accessible.) Names and other information aboutsimilar-appearing persons located via the searching are returned to theuser's cell phone—to help refresh the user's memory.

Various UI procedures are contemplated. When data is returned from theremote service, the user may push button 16 b to scroll thru matches inorder of closest-similarity—regardless of geography. Thumbnails of thematched individuals with associated name and other profile informationcan be displayed, or just full screen images of the person can bepresented—with the name overlaid. When the familiar person isrecognized, the user may press button 18 to load the fullFaceBook/MySpace/Linked-In page for that person. Alternatively, insteadof presenting images with names, just a textual list of name may bepresented, e.g., all on a single screen—ordered by similarity offace-match; SMS text messaging can suffice for this last arrangement.

Pushing button 16 d may scroll thru matches in order ofclosest-similarity, of people who list their residence as within acertain geographical proximity (e.g., same metropolitan area, samestate, same campus, etc.) of the user's present location or the user'sreference location (e.g., home). Pushing button 16 a may yield a similardisplay, but limited to persons who are “Friends” of the user within asocial network (or who are Friends of Friends, or who are within anotherspecified degree of separation of the user).

A related arrangement is a law enforcement tool in which an officercaptures an image of a person and submits same to a database containingfacial portrait/eigenvalue information from government driver licenserecords and/or other sources. Pushing button 16 b causes the screen todisplay a sequence of images/biographical dossiers about personsnationwide having the closest facial matches. Pushing button 16 d causesthe screen to display a similar sequence, but limited to persons withinthe officer's state. Button 16 a yields such a sequence, but limited topersons within the metropolitan area in which the officer is working.

Instead of three dimensions of information browsing (buttons 16 b, 16 d,16 a, e.g., for similar-appearing images/similarly locatedimages/similar metadata-tagged images), more or less dimensions can beemployed. FIG. 25B shows browsing screens in just two dimensions.(Pressing the right button yields a first sequence 40 of informationscreens; pressing the left button yields a different sequence 42 ofinformation screens.)

Instead of two or more distinct buttons, a single UI control can beemployed to navigate in the available dimensions of information. Ajoystick is one such device. Another is a roller wheel (or scrollwheel). Portable device 10 of FIG. 24 has a roller wheel 24 on its side,which can be rolled-up or rolled-down. It can also be pressed-in to makea selection (e.g., akin to buttons 16 c or 18 of the earlier-discussedcontroller). Similar controls are available on many mice.

In most user interfaces, opposing buttons (e.g., left button 16 b, andright button 16 d) navigate the same dimension of information—just inopposite directions (e.g., forward/reverse). In the particular interfacediscussed above, it will be recognized that this is not the case(although in other implementations, it may be so). Pressing the rightbutton 16 b, and then pressing the left button 16 d, does not return thesystem to its original state. Instead, pressing the right button gives,e.g., a first similar-appearing image, and pressing the left buttongives the first similarly-located image.

Sometimes it is desirable to navigate through the same sequence ofscreens, but in reverse of the order just-reviewed. Various interfacecontrols can be employed to do this.

One is a “Reverse” button. The device 10 in FIG. 24 includes a varietyof buttons not-yet discussed (e.g., buttons 20 a-20 f, around theperiphery of the controller 14). Any of these—if pressed—can serve toreverse the scrolling order. By pressing, e.g., button 20 a, thescrolling (presentation) direction associated with nearby button 16 bcan be reversed. So if button 16 b normally presents items in order ofincreasing cost, activation of button 20 a can cause the function ofbutton 16 b to switch, e.g., to presenting items in order of decreasingcost. If, in reviewing screens resulting from use of button 16 b, theuser “overshoots” and wants to reverse direction, she can push button 20a, and then push button 16 b again. The screen(s) earlier presentedwould then appear in reverse order—starting from the present screen.

Or, operation of such a button (e.g., 20 a or 20 f) can cause theopposite button 16 d to scroll back thru the screens presented byactivation of button 16 b, in reverse order.

A textual or symbolic prompt can be overlaid on the display screen inall these embodiments—informing the user of the dimension of informationthat is being browsed, and the direction (e.g., browsing by cost:increasing).

In still other arrangements, a single button can perform multiplefunctions. For example, pressing button 16 b can cause the system tostart presenting a sequence of screens, e.g., showing pictures of housesfor sale nearest the user's location—presenting each for 800milliseconds (an interval set by preference data entered by the user).Pressing button 16 b a second time can cause the system to stop thesequence—displaying a static screen of a house for sale. Pressing button16 b a third time can cause the system to present the sequence inreverse order, starting with the static screen and going backwards thruthe screens earlier presented. Repeated operation of buttons 16 a, 16 b,etc., can operate likewise (but control different sequences ofinformation, e.g., houses closest in price, and houses closest infeatures).

In arrangements in which the presented information stems from a processapplied to a base image (e.g., a picture snapped by a user), this baseimage may be presented throughout the display—e.g., as a thumbnail in acorner of the display. Or a button on the device (e.g., 26 a, or 20 b)can be operated to immediately summon the base image back to thedisplay.

Touch interfaces are gaining in popularity, such as in productsavailable from Apple and Microsoft (detailed, e.g., in patentpublications 20060026535, 20060026536, 20060250377, 20080211766,20080158169, 20080158172, 20080001924, 20080204426, 20080174570,20070236470 and 20060033701). Such technologies can be employed toenhance and extend the just-reviewed user interface concepts—allowinggreater degrees of flexibility and control. Each button press notedabove can have a counterpart gesture in the vocabulary of the touchscreen system.

For example, different touch-screen gestures can invoke display of thedifferent types of image feeds just reviewed. A brushing gesture to theright, for example, may present a rightward-scrolling series of imageframes 30 of imagery having similar visual content (with the initialspeed of scrolling dependent on the speed of the user gesture, and withthe scrolling speed decelerating—or not—over time). A brushing gestureto the left may present a similar leftward-scrolling display of imagery32 having similar GPS information. A brushing gesture upward may presentimages an upward-scrolling display of imagery 34 similar in metadata. Atany point the user can tap one of the displayed images to make it thebase image, with the process repeating.

Other gestures can invoke still other actions. One such action isdisplaying overhead imagery corresponding to the GPS location associatedwith a selected image. The imagery can be zoomed in/out with othergestures. The user can select for display photographic imagery, mapdata, data from different times of day or different dates/seasons,and/or various overlays (topographic, places of interest, and otherdata, as is known from Google Earth), etc. Icons or other graphics maybe presented on the display depending on contents of particular imagery.One such arrangement is detailed in U.S. Pat. No. 8,565,815.

“Curbside” or “street-level” imagery—rather than overhead imagery—can bealso displayed.

It will be recognized that certain embodiments of the present technologyinclude a shared general structure. An initial set of data (e.g., animage, or metadata such as descriptors or geocode information, or imagemetrics such as eigenvalues) is presented. From this, a second set ofdata (e.g., images, or image metrics, or metadata) are obtained. Fromthat second set of data, a third set of data is compiled (e.g., imageswith similar image metrics or similar metadata, or image metrics, ormetadata). Items from the third set of data can be used as a result ofthe process, or the process may continue, e.g., by using the third setof data in determining fourth data (e.g., a set of descriptive metadatacan be compiled from the images of the third set). This can continue,e.g., determining a fifth set of data from the fourth (e.g., identifyinga collection of images that have metadata terms from the fourth dataset). A sixth set of data can be obtained from the fifth (e.g.,identifying clusters of GPS data with which images in the fifth set aretagged), and so on.

The sets of data can be images, or they can be other forms of data(e.g., image metrics, textual metadata, geolocation data, decoded OCR-,barcode-, watermark-data, etc).

Any data can serve as the seed. The process can start with image data,or with other information, such as image metrics, textual metadata (akasemantic metadata), geolocation information (e.g., GPS coordinates),decoded OCR/barcode/watermark data, etc. From a first type ofinformation (image metrics, semantic metadata, GPS info, decoded info),a first set of information-similar images can be obtained. From thatfirst set, a second, different type of information (imagemetrics/semantic metadata/GPS/decoded info, etc.) can be gathered. Fromthat second type of information, a second set of information-similarimages can be obtained. From that second set, a third, different type ofinformation (image metrics/semantic metadata/GPS/decoded info, etc.) canbe gathered. From that third type of information, a third set ofinformation-similar images can be obtained. Etc.

Thus, while the illustrated embodiments generally start with an image,and then proceed by reference to its image metrics, and so on, entirelydifferent combinations of acts are also possible. The seed can be thepayload from a product barcode. This can generate a first collection ofimages depicting the same barcode. This can lead to a set of commonmetadata. That can lead to a second collection of images based on thatmetadata. Image metrics may be computed from this second collection, andthe most prevalent metrics can be used to search and identify a thirdcollection of images. The images thus identified can be presented to theuser using the arrangements noted above.

In some embodiments, the present technology may be regarded as employingan iterative, recursive process by which information about one set ofimages (a single image in many initial cases) is used to identify asecond set of images, which may be used to identify a third set ofimages, etc. The function by which each set of images is related to thenext relates to a particular class of image information, e.g., imagemetrics, semantic metadata, GPS, decoded info, etc.

In other contexts, the relation between one set of images and the nextis a function not just of one class of information, but two or more. Forexample, a seed user image may be examined for both image metrics andGPS data. From these two classes of information a collection of imagescan be determined—images that are similar in both some aspect of visualappearance and location. Other pairings, triplets, etc., ofrelationships can naturally be employed—in the determination of any ofthe successive sets of images.

Further Discussion

Some embodiments of the present technology analyze a consumer cell phonepicture, and heuristically determine information about the picture'ssubject. For example, is it a person, place, or thing? From this highlevel determination, the system can better formulate what type ofresponse might be sought by the consumer—making operation moreintuitive.

For example, if the subject of the photo is a person, the consumer mightbe interested in adding the depicted person as a FaceBook “friend.” Orsending a text message to that person. Or publishing an annotatedversion of the photo to a web page. Or simply learning who the personis.

If the subject is a place (e.g., Times Square), the consumer might beinterested in the local geography, maps, and nearby attractions.

If the subject is a thing (e.g., the Liberty Bell or a bottle of beer),the consumer may be interested in information about the object (e.g.,its history, others who use it), or in buying or selling the object,etc.

Based on the image type, an illustrative system/service can identify oneor more actions that it expects the consumer will find mostappropriately responsive to the cell phone image. One or all of thesecan be undertaken, and cached on the consumer's cell phone for review.For example, scrolling a thumbwheel on the side of the cell phone maypresent a succession of different screens—each with differentinformation responsive to the image subject. (Or a screen may bepresented that queries the consumer as to which of a few possibleactions is desired.)

In use, the system can monitor which of the available actions is chosenby the consumer. The consumer's usage history can be employed to refinea Bayesian model of the consumer's interests and desires, so that futureresponses can be better customized to the user.

These concepts will be clearer by example (aspects of which aredepicted, e.g., in FIGS. 26 and 27).

Processing a Set of Sample Images

Assume a tourist snaps a photo of the Prometheus statue at RockefellerCenter in New York using a cell phone or other mobile device. Initially,it is just a bunch of pixels. What to do?

Assume the image is geocoded with location information (e.g.,latitude/longitude in XMP- or EXIF-metadata).

From the image geocode data, a search of Flickr can be undertaken for afirst set of images—taken from the same (or nearby) location. Perhapsthere are 5 or 500 images in this first set.

Metadata from this set of images is collected. The metadata can be ofvarious types. One is words/phrases from a title given to an image.Another is information in metatags assigned to the image—usually by thephotographer (e.g., naming the photo subject and certainattributes/keywords), but additionally by the capture device (e.g.,identifying the camera model, the date/time of the photo, the location,etc). Another is words/phrases in a narrative description of the photoauthored by the photographer.

Some metadata terms may be repeated across in different images.Descriptors common to two or more images can be identified (clustered),and the most popular terms may be ranked. (Such as listing is shown at“A” in FIG. 26A. Here, and in other metadata listings, only partialresults are given for expository convenience.)

From the metadata, and from other analysis, it may be possible todetermine which images in the first set are likely person-centric, whichare place-centric, and which are thing-centric.

Consider the metadata with which a set of 50 images may be tagged. Someof the terms relate to place. Some relate to persons depicted in theimages. Some relate to things.

Place-Centric Processing

Terms that relate to place can be identified using various techniques.One is to use a database with geographical information to look-uplocation descriptors near a given geographical position. Yahoo'sGeoPlanet service, for example, returns a hierarchy of descriptors suchas “Rockefeller Center,” “10024” (a zip code), “Midtown Manhattan,” “NewYork,” “Manhattan,” “New York,” and “United States,” when queried withthe latitude/longitude of the Rockefeller Center.

The same service can return names of adjoining/siblingneighborhoods/features on request, e.g., “10017,” “10020,” “10036,”“Theater District,” “Carnegie Hall,” “Grand Central Station,” “Museum ofAmerican Folk Art,” etc., etc.

Nearby street names can be harvested from a variety of mapping programs,given a set of latitude/longitude coordinates or other location info.

A glossary of nearby place-descriptors can be compiled in such manner.The metadata harvested from the set of Flickr images can then beanalyzed, by reference to the glossary, to identify the terms thatrelate to place (e.g., that match terms in the glossary).

Consideration then turns to use of these place-related metadata in thereference set of images collected from Flickr.

Some images may have no place-related metadata. These images are likelyperson-centric or thing-centric, rather than place-centric.

Other images may have metadata that is exclusively place-related. Theseimages are likely place-centric, rather than person-centric orthing-centric.

In between are images that have both place-related metadata, and othermetadata. Various rules can be devised and utilized to assign therelative relevance of the image to place.

One rule looks at the number of metadata descriptors associated with animage, and determines the fraction that is found in the glossary ofplace-related terms. This is one metric.

Another looks at where in the metadata the place-related descriptorsappear. If they appear in an image title, they are likely more relevantthan if they appear at the end of a long narrative description about thephotograph. Placement of the placement-related metadata is anothermetric.

Consideration can also be given to the particularity of theplace-related descriptor. A descriptor “New York” or “USA” may be lessindicative that an image is place-centric than a more particulardescriptor, such as “Rockefeller Center” or “Grand Central Station.”This can yield a third metric.

A related, fourth metric considers the frequency of occurrence (orimprobability) of a term—either just within the collected metadata, orwithin a superset of that data. “RCA Building” is more relevant, fromthis standpoint, than “Rockefeller Center” because it is used much lessfrequently.

These and other metrics can be combined to assign each image in the setwith a place score indicating its potential place-centric-ness.

The combination can be a straight sum of four factors, each ranging from0 to 100. More likely, however, some metrics will be weighted moreheavily. The following equation employing metrics M1, M2, M2 and M4 canbe employed to yield a score S, with the factors A, B, C, D andexponents W, X, Y and Z determined experimentally, or by Bayesiantechniques:S=(A*M1)^(W)+(B*M2)^(X)+(C*M3)^(Y)+(D*M4)^(Z)

Person-Centric Processing

A different analysis can be employed to estimate the person-centric-nessof each image in the set obtained from Flickr.

As in the example just-given, a glossary of relevant terms can becompiled—this time terms associated with people. In contrast to theplace name glossary, the person name glossary can be global—rather thanassociated with a particular locale. (However, different glossaries maybe appropriate in different countries.)

Such a glossary can be compiled from various sources, includingtelephone directories, lists of most popular names, and other referenceworks where names appear. The list may start, “Aaron, Abigail, Adam,Addison, Adrian, Aidan, Aiden, Alex, Alexa, Alexander, Alexandra,Alexis, Allison, Alyssa, Amelia, Andrea, Andrew, Angel, Angelina, Anna,Anthony, Antonio, Ariana, Arianna, Ashley, Aubrey, Audrey, Austin,Autumn, Ava, Avery . . . ”

First names alone can be considered, or last names can be consideredtoo. (Some names may be a place name or a person name. Searching foradjoining first/last names and/or adjoining place names can helpdistinguish ambiguous cases. E.g., Elizabeth Smith is a person;Elizabeth N.J. is a place.)

Personal pronouns and the like can also be included in such a glossary(e.g., he, she, him, her, his, our, her, I, me, myself, we, they, them,mine, their). Nouns identifying people and personal relationships canalso be included (e.g., uncle, sister, daughter, gramps, boss, student,employee, wedding, etc)

Adjectives and adverbs that are usually applied to people may also beincluded in the person-term glossary (e.g., happy, boring, blonde, etc),as can the names of objects and attributes that are usually associatedwith people (e.g., t-shirt, backpack, sunglasses, tanned, etc.). Verbsassociated with people can also be employed (e.g., surfing, drinking).

In this last group, as in some others, there are some terms that couldalso apply to thing-centric images (rather than person-centric). Theterm “sunglasses” may appear in metadata for an image depictingsunglasses, alone; “happy” may appear in metadata for an image depictinga dog. There are also some cases where a person-term may also be aplace-term (e.g., Boring, Oreg.). In more sophisticated embodiments,glossary terms can be associated with respective confidence metrics, bywhich any results based on such terms may be discounted or otherwiseacknowledged to have different degrees of uncertainty.)

As before, if an image is not associated with any person-relatedmetadata, then the image can be adjudged likely not person-centric.Conversely, if all of the metadata is person-related, the image islikely person-centric.

For other cases, metrics like those reviewed above can be assessed andcombined to yield a score indicating the relative person-centric-ness ofeach image, e.g., based on the number, placement, particularity and/orfrequency/improbability of the person-related metadata associated withthe image.

While analysis of metadata gives useful information about whether animage is person-centric, other techniques can also be employed—eitheralternatively, or in conjunction with metadata analysis.

One technique is to analyze the image looking for continuous areas ofskin-tone colors. Such features characterize many features ofperson-centric images, but are less frequently found in images of placesand things.

A related technique is facial recognition. This science has advanced tothe point where even inexpensive point-and-shoot digital cameras canquickly and reliably identify faces within an image frame (e.g., tofocus or expose the image based on such subjects).

(Face finding technology is detailed, e.g., in U.S. Pat. Nos. 5,781,650,5,835,616, 6,633,655, 6,597,801 and 6,430,306, and in Yang et al,Detecting Faces in Images: A Survey, IEEE Transactions on PatternAnalysis and Machine Intelligence, Vol. 24, No. 1, January 2002, pp.34-58, and Zhao, et al, Face Recognition: A Literature Survey, ACMComputing Surveys, 2003, pp. 399-458. Additional papers about facialrecognition technologies are noted in a bibliography at the end of thisspecification.)

Facial recognition algorithms can be applied to the set of referenceimages obtained from Flickr, to identify those that have evident faces,and identify the portions of the images corresponding to the faces.

Of course, many photos have faces depicted incidentally within the imageframe. While all images having faces could be identified asperson-centric, most embodiments employ further processing to provide amore refined assessment.

One form of further processing is to determine the percentage area ofthe image frame occupied by the identified face(s). The higher thepercentage, the higher the likelihood that the image is person-centric.This is another metric than can be used in determining an image'sperson-centric score.

Another form of further processing is to look for the existence of (1)one or more faces in the image, together with (2) person-descriptors inthe metadata associated with the image. In this case, the facialrecognition data can be used as a “plus” factor to increase aperson-centric score of an image based on metadata or other analysis.(The “plus” can take various forms. E.g., a score (in a 0-100 scale) canbe increased by 10, or increased by 10%. Or increased by half theremaining distance to 100, etc.)

Thus, for example, a photo tagged with “Elizabeth” metadata is morelikely a person-centric photo if the facial recognition algorithm findsa face within the image than if no face is found.

(Conversely, the absence of any face in an image can be used as a “plus”factor to increase the confidence that the image subject is of adifferent type, e.g., a place or a thing. Thus, an image tagged withElizabeth as metadata, but lacking any face, increases the likelihoodthat the image relates to a place named Elizabeth, or a thing namedElizabeth—such as a pet.)

Still more confidence in the determination can be assumed if the facialrecognition algorithm identifies a face as a female, and the metadataincludes a female name. Such an arrangement, of course, requires thatthe glossary—or other data structure—have data that associates genderswith at least some names.

(Still more sophisticated arrangements can be implemented. For example,the age of the depicted person(s) can be estimated using automatedtechniques (e.g., as detailed in U.S. Pat. No. 5,781,650). Names foundin the image metadata can also be processed to estimate the age of thethus-named person(s). This can be done using public domain informationabout the statistical distribution of a name as a function of age (e.g.,from published Social Security Administration data, and web sites thatdetail most popular names from birth records). Thus, names Mildred andGertrude may be associated with an age distribution that peaks at age80, whereas Madison and Alexis may be associated with an agedistribution that peaks at age 8. Finding statistically-likelycorrespondence between metadata name and estimated person age canfurther increase the person-centric score for an image. Statisticallyunlikely correspondence can be used to decrease the person-centricscore. (Estimated information about the age of a subject in theconsumer's image can also be used to tailor the intuited response(s), asmay information about the subject's gender.))

Just as detection of a face in an image can be used as a “plus” factorin a score based on metadata, the existence of person-centric metadatacan be used as a “plus” factor to increase a person-centric score basedon facial recognition data.

Of course, if no face is found in an image, this information can be usedto reduce a person-centric score for the image (perhaps down to zero).

Thing-Centric Processing

A thing-centered image is the third type of image that may be found inthe set of images obtained from Flickr in the present example. There arevarious techniques by which a thing-centric score for an image can bedetermined.

One technique relies on metadata analysis, using principles like thosedetailed above. A glossary of nouns can be compiled—either from theuniverse of Flickr metadata or some other corpus (e.g., WordNet), andranked by frequency of occurrence. Nouns associated with place andpersons can be removed from the glossary. The glossary can be used inthe manners identified above to conduct analyses of the images'metadata, to yield a score for each.

Another approach uses pattern matching to identify thing-centricimages—matching each against a library of known thing-related images.

Still another approach is based on earlier-determined scores forperson-centric and place-centric. A thing-centric score may be assignedin inverse relationship to the other two scores (i.e., if an imagescores low for being person-centric, and low for being place-centric,then it can be assigned a high score for being thing-centric).

Such techniques may be combined, or used individually. In any event, ascore is produced for each image—tending to indicate whether the imageis more- or less-likely to be thing-centric.

Further Processing of Sample Set of Images

Data produced by the foregoing techniques can produce three scores foreach image in the set, indicating roughconfidence/probability/likelihood that the image is (1) person-centric,(2) place-centric, or (3) thing-centric. These scores needn't add to100% (although they may). Sometimes an image may score high in two ormore categories. In such case the image may be regarded as havingmultiple relevance, e.g., as both depicting a person and a thing.

The set of images downloaded from Flickr may next be segregated intogroups, e.g., A, B and C, depending on whether identified as primarilyperson-centric, place-centric, or thing-centric. However, since someimages may have split probabilities (e.g., an image may have someindicia of being place-centric, and some indicia of beingperson-centric), identifying an image wholly by its high score ignoresuseful information. Preferable is to calculate a weighted score for theset of images—taking each image's respective scores in the threecategories into account.

A sample of images from Flickr—all taken near Rockefeller Center—maysuggest that 60% are place-centric, 25% are person-centric, and 15% arething-centric.

This information gives useful insight into the tourist's cell phoneimage—even without regard to the contents of the image itself (exceptits geocoding). That is, chances are good that the image isplace-centric, with less likelihood it is person-centric, and still lessprobability it is thing centric. (This ordering can be used to determinethe order of subsequent steps in the process—allowing the system to morequickly gives responses that are most likely to be appropriate.)

This type-assessment of the cell phone photo can be used—alone—to helpdetermine an automated action provided to the tourist in response to theimage. However, further processing can better assess the image'scontents, and thereby allow a more particularly-tailored action to beintuited.

Similarity Assessments and Metadata Weighting

Within the set of co-located images collected from Flickr, images thatare place-centric will tend to have a different appearance than imagesthat are person-centric or thing-centric, yet tend to have somesimilarity within the place-centric group. Place-centric images may becharacterized by straight lines (e.g., architectural edges). Orrepetitive patterns (windows). Or large areas of uniform texture andsimilar color near the top of the image (sky).

Images that are person-centric will also tend to have differentappearances than the other two classes of image, yet have commonattributes within the person-centric class. For example, person-centricimages will usually have faces—generally characterized by two eyes and anose, areas of flesh tones, etc.

Although thing-centric images are perhaps the most diverse, images fromany given geography may tend to have unifying attributes or features.Photos geocoded at a horse track will depict horses with some frequency;photos geocoded from Independence National Historical Park inPhiladelphia will tend to depict the Liberty Bell regularly, etc.

By determining whether the cell phone image is more similar toplace-centric, or person-centric, or thing-centric images in the set ofFlickr images, more confidence in the subject of the cell phone imagecan be achieved (and a more accurate response can be intuited andprovided to the consumer).

A fixed set of image assessment criteria can be applied to distinguishimages in the three categories. However, the detailed embodimentdetermines such criteria adaptively. In particular, this embodimentexamines the set of images and determines which imagefeatures/characteristics/metrics most reliably (1) grouplike-categorized images together (similarity); and (2) distinguishdifferently-categorized images from each other (difference). Among theattributes that may be measured and checked for similarity/differencebehavior within the set of images are dominant color; color diversity;color histogram; dominant texture; texture diversity; texture histogram;edginess; wavelet-domain transform coefficient histograms, and dominantwavelet coefficients; frequency domain transfer coefficient histogramsand dominant frequency coefficients (which may be calculated indifferent color channels); eigenvalues; geometric class probabilities;symmetry; percentage of image area identified as facial; imageautocorrelation; low-dimensional “gists” of image; etc. (Combinations ofsuch metrics may be more reliable than the characteristicsindividually.)

One way to determine which metrics are most salient for these purposesis to compute a variety of different image metrics for the referenceimages. If the results within a category of images for a particularmetric are clustered (e.g., if, for place-centric images, the colorhistogram results are clustered around particular output values), and ifimages in other categories have few or no output values near thatclustered result, then that metric would appear well suited for use asan image assessment criteria. (Clustering is commonly performed using animplementation of a k-means algorithm.)

In the set of images from Rockefeller Center, the system may determinethat an edginess score of >40 is reliably associated with images thatscore high as place-centric; a facial area score of >15% is reliablyassociated with images that score high as person-centric; and a colorhistogram that has a local peak in the gold tones—together with afrequency content for yellow that peaks at lower image frequencies, issomewhat associated with images that score high as thing-centric.

The analysis techniques found most useful in grouping/distinguishing thedifferent categories of images can then be applied to the user's cellphone image. The results can then be analyzed for proximity—in adistance measure sense (e.g., multi-dimensional space)—with thecharacterizing features associated with different categories of images.(This is the first time that the cell phone image has been processed inthis particular embodiment.)

Using such techniques, the cell phone image may score a 60 forthing-centric, a 15 for place-centric, and a 0 for person-centric (onscale of 0-100). This is a second, better set of scores that can be usedto classify the cell phone image (the first being the statisticaldistribution of co-located photos found in Flickr).

The similarity of the user's cell phone image may next be compared withindividual images in the reference set. Similarity metrics identifiedearlier can be used, or different measures can be applied. The time orprocessing devoted to this task can be apportioned across the threedifferent image categories based on the just-determined scores. E.g.,the process may spend no time judging similarity with reference imagesclassed as 100% person-centric, but instead concentrate on judgingsimilarity with reference images classed as thing- or place-centric(with four times as much effort being applied to the former than thelatter). A similarly score is generated for most of the images in thereference set (excluding those that are assessed as 100%person-centric).

Consideration then returns to metadata. Metadata from the referenceimages is again assembled—this time weighted in accordance with eachimage's respective similarity to the cell phone image. (The weightingcan be linear or exponential.) Since metadata from similar images isweighted more than metadata from dissimilar images, the resulting set ofmetadata is tailored to more likely correspond to the cell phone image.

From the resulting set, the top N (e.g., 3) metadata descriptors may beused. Or descriptors that—on a weighted basis—comprise an aggregate M %of the metadata set.

In the example given, the thus-identified metadata may comprise“Rockefeller Center,” “Prometheus,” and “Skating rink,” with respectivescores of 19, 12 and 5 (see “B” in FIG. 26B).

With this weighted set of metadata, the system can begin determiningwhat responses may be most appropriate for the consumer. In theexemplary embodiment, however, the system continues by further refiningits assessment of the cell phone image. (The system may begindetermining appropriate responses while also undertaking the furtherprocessing.)

Processing a Second Set of Reference Images

At this point the system is better informed about the cell phone image.Not only is its location known; so is its likely type (thing-centric)and some of its most-probably-relevant metadata. This metadata can beused in obtaining a second set of reference images from Flickr.

In the illustrative embodiment, Flickr is queried for images having theidentified metadata. The query can be geographically limited to the cellphone's geolocation, or a broader (or unlimited) geography may besearched. (Or the query may run twice, so that half of the images areco-located with the cell phone image, and the others are remote, etc.)

The search may first look for images that are tagged with all of theidentified metadata. In this case, 60 images are found. If more imagesare desired, Flickr may be searched for the metadata terms in differentpairings, or individually. (In these latter cases, the distribution ofselected images may be chosen so that the metadata occurrence in theresults corresponds to the respective scores of the different metadataterms, i.e., 19/12/5.)

Metadata from this second set of images can be harvested, clustered, andmay be ranked (“C” in FIG. 26B). (Noise words (“and, of, or,” etc.) canbe eliminated. Words descriptive only of the camera or the type ofphotography may also be disregarded (e.g., “Nikon,” “D80,” “HDR,” “blackand white,” etc.). Month names may also be removed.)

The analysis performed earlier—by which each image in the first set ofimages was classified as person-centric, place-centric orthing-centric—can be repeated on images in the second set of images.Appropriate image metrics for determining similarity/difference withinand between classes of this second image set can be identified (or theearlier measures can be employed). These measures are then applied, asbefore, to generate refined scores for the user's cell phone image, asbeing person-centric, place-centric, and thing-centric. By reference tothe images of the second set, the cell phone image may score a 65 forthing-centric, 12 for place-centric, and 0 for person-centric. (Thesescores may be combined with the earlier-determined scores, e.g., byaveraging, if desired.)

As before, similarity between the user's cell phone image and each imagein the second set can be determined. Metadata from each image can thenbe weighted in accordance with the corresponding similarity measure. Theresults can then be combined to yield a set of metadata weighted inaccordance image similarity.

Some of the metadata—often including some highly ranked terms—will be ofrelatively low value in determining image-appropriate responses forpresentation to the consumer. “New York,” “Manhattan,” are a fewexamples. Generally more useful will be metadata descriptors that arerelatively unusual.

A measure of “unusualness” can be computed by determining the frequencyof different metadata terms within a relevant corpus, such as Flickrimage tags (globally, or within a geolocated region), or image tags byphotographers from whom the respective images were submitted, or wordsin an encyclopedia, or in Google's index of the web, etc. The terms inthe weighted metadata list can be further weighted in accordance withtheir unusualness (i.e., a second weighting).

The result of such successive processing may yield the list of metadatashown at “D” in FIG. 26B (each shown with its respective score). Thisinformation (optionally in conjunction with a tag indicating theperson/place/thing determination) allows responses to the consumer to bewell-correlated with the cell phone photo.

It will be recognized that this set of inferred metadata for the user'scell phone photo was compiled entirely by automated processing of otherimages, obtained from public sources such as Flickr, in conjunction withother public resources (e.g., listings of names, places). The inferredmetadata can naturally be associated with the user's image. Moreimportantly for the present application, however, it can help a serviceprovider decide how best to respond to submission of the user's image.

Determining Appropriate Responses for Consumer

Referring to FIG. 30, the system just-described can be viewed as oneparticular application of an “image juicer” that receives image datafrom a user, and applies different forms of processing so as to gather,compute, and/or infer information that can be associated with the image.

As the information is discerned, it can be forwarded by a router todifferent service providers. These providers may be arranged to handledifferent types of information (e.g., semantic descriptors, imagetexture data, etc) or to different classes of images (e.g., photo offriend, photo of a can of soda, etc). Outputs from these serviceproviders are sent to one or more devices (e.g., the user's cell phone)for presentation or later reference. The present discussion nowconsiders how these service providers decide what responses may beappropriate for a given set of input information.

One approach is to establish a taxonomy of image subjects andcorresponding responses. A tree structure can be used, with an imagefirst being classed into one of a few high level groupings (e.g.,person/place/thing), and then each group being divided into furthersubgroups. In use, an image is assessed through different branches ofthe tree until the limits of available information allow no furtherprogress to be made. Actions associated with the terminal leaf or nodeof the tree are then taken.

Part of a simple tree structure is shown in FIG. 31. (Each node spawnsthree branches, but this is for illustration only; more or less branchescan of course be used.)

If the subject of the image is inferred to be an item of food (e.g., ifthe image is associated with food-related metadata), three differentscreens of information can be cached on the user's phone. One starts anonline purchase of the depicted item at an online vendor. (The choice ofvendor, and payment/shipping details, are obtained from user profiledata.) The second screen shows nutritional information about theproduct. The third presents a map of the local area—identifying storesthat sell the depicted product. The user switches among these responsesusing a roller wheel 24 on the side of the phone (FIG. 24).

If the subject is inferred to be a photo of a family member or friend,one screen presented to the user gives the option of posting a copy ofthe photo to the user's FaceBook page, annotated with the person(s)'slikely name(s). (Determining the names of persons depicted in a photocan be done by submitting the photo to the user's account at Picasa.Picasa performs facial recognition operations on submitted user images,and correlates facial eigenvectors with individual names provided by theuser, thereby compiling a user-specific database of facial recognitioninformation for friends and others depicted in the user's prior images.)Another screen starts a text message to the individual, with theaddressing information having been obtained from the user's addressbook, indexed by the Picasa-determined identity. The user can pursue anyor all of the presented options by switching between the associatedscreens.

If the subject appears to be a stranger (e.g., not recognized byPicasa), the system will have earlier undertaken an attemptedrecognition of the person using publicly available facial recognitioninformation. (Such information can be extracted from photos of knownpersons. VideoSurf is one vendor with a database of facial recognitionfeatures for actors and other persons.) The screen(s) presented to theuser can show reference photos of the persons matched (together with a“match” score), as well as dossiers of associated information compiledfrom the web and other databases. A further screen gives the user theoption of sending a “Friend” invite to the recognized person on MySpace,or another social networking site where the recognized person is foundto have a presence. A still further screen details the degree ofseparation between the user and the recognized person. (E.g., my brotherDavid has a classmate Steve, who has a friend Matt, who has a friendTom, who is the son of the depicted person.) Such relationships can bedetermined from association information published on social networkingsites.

Of course, the responsive options contemplated for the differentsub-groups of image subjects may meet most user desires, but some userswill want something different. Thus, at least one alternative responseto each image will typically be open-ended—allowing the user to navigateto different information, or specify a desired response—making use ofwhatever image/metadata processed information is available.

One such open-ended approach is to submit the twice-weighted metadatanoted above (e.g., “D” in FIG. 26B) to a general purpose search engine.Google, per se, is not necessarily best for this function, becausecurrent Google searches require that all search terms be found in theresults. Better is a search engine that does fuzzy searching, and isresponsive to differently-weighted keywords—not all of which need befound. The results can indicate different seeming relevance, dependingon which keywords are found, where they are found, etc. (A resultincluding “Prometheus” but lacking “RCA Building” would be ranked morerelevant than a result including the latter but lacking the former.)

The results from such a search can be clustered by other concepts. Forexample, some of the results may be clustered because they share thetheme “art deco.” Others may be clustered because they deal withcorporate history of RCA and GE. Others may be clustered because theyconcern the works of the architect Raymond Hood. Others may be clusteredas relating to 20^(th) century American sculpture, or Paul Manship.Other concepts found to produce distinct clusters may include JohnRockefeller, The Mitsubishi Group, Colombia University, Radio City MusicHall, The Rainbow Room Restaurant, etc.

Information from these clusters can be presented to the user onsuccessive UI screens, e.g., after the screens on which prescribedinformation/actions are presented. The order of these screens can bedetermined by the sizes of the information clusters, or thekeyword-determined relevance.

Still a further response is to present to the user a Google searchscreen—pre-populated with the twice-weighted metadata as search terms.The user can then delete terms that aren't relevant to his/her interest,and add other terms, so as to quickly execute a web search leading tothe information or action desired by the user.

In some embodiments, the system response may depend on people with whomthe user has a “friend” relationship in a social network, or some otherindicia of trust. For example, if little is known about user Ted, butthere is a rich set of information available about Ted's friend Alice,that rich set of information may be employed in determining how torespond to Ted, in connection with a given content stimulus.

Similarly, if user Ted is a friend of user Alice, and Bob is a friend ofAlice, then information relating to Bob may be used in determining anappropriate response to Ted.

The same principles can be employed even if Ted and Alice are strangers,provided there is another basis for implicit trust. While basic profilesimilarity is one possible basis, a better one is the sharing an unusualattribute (or, better, several). Thus, for example, if both Ted andAlice share the traits of being fervent supporters of Dennis Kucinichfor president, and being devotees of pickled squid, then informationrelating to one might be used in determining an appropriate response topresent to the other.

The arrangements just-described provides powerful new functionality.However, the “intuiting” of the responses likely desired by the userrely largely on the system designers. They consider the different typesof images that may be encountered, and dictate responses (or selectionsof responses) that they believe will best satisfy the users' likelydesires.

In this respect the above-described arrangements are akin to earlyindexes of the web—such as Yahoo! Teams of humans generated taxonomiesof information for which people might search, and then manually locatedweb resources that could satisfy the different search requests.

Eventually the web overwhelmed such manual efforts at organization.Google's founders were among those that recognized that an untappedwealth of information about the web could be obtained from examininglinks between the pages, and actions of users in navigating these links.Understanding of the system thus came from data within the system,rather than from an external perspective.

In like fashion, manually crafted trees of imageclassifications/responses will probably someday be seen as an earlystage in the development of image-responsive technologies. Eventuallysuch approaches will be eclipsed by arrangements that rely on machineunderstanding derived from the system itself, and its use.

One such technique simply examines which responsive screen(s) areselected by users in particular contexts. As such usage patterns becomeevident, the most popular responses can be moved earlier in the sequenceof screens presented to the user.

Likewise, if patterns become evident in use of the open-ended searchquery option, such action can become a standard response, and movedhigher in the presentation queue.

The usage patterns can be tailored in various dimensions of context.Males between 40 and 60 years of age, in New York, may demonstrateinterest in different responses following capture of a snapshot of astatue by a 20^(th) century sculptor, than females between 13 and 16years of age in Beijing. Most persons snapping a photo of a foodprocessor in the weeks before Christmas may be interested in finding thecheapest online vendor of the product; most persons snapping a photo ofthe same object the week following Christmas may be interested inlisting the item for sale on E-Bay. Etc. Desirably, usage patterns aretracked with as many demographic and other descriptors as possible, soas to be most-predictive of user behavior.

More sophisticated techniques can also be applied, drawing from the richsources of expressly- and inferentially-linked data sources nowavailable. These include not only the web and personal profileinformation, but all manner of other digital data we touch and in whichwe leave traces, e.g., cell phone billing statements, credit cardstatements, shopping data from Amazon, EBay, Google search history,browsing history, cached web pages, cookies, email archives, travelreservations on Expedia and Orbitz, music collections on iTunes, cabletelevision subscriptions, Netflix movie choices, GPS trackinginformation, social network data and activities, activities and postingson photo sites such as Flickr and Picasa, and video sites such asYouTube; the times of day memorialized in these records, etc. (our“digital life log”). Moreover, this information is potentially availablenot just for the user, but also for the user's friends/family, forothers having demographic similarities with the user, and ultimatelyeveryone else (with appropriate anonymization and/or privacysafeguards).

The network of interrelationships between these data sources is smallerthan the network of web links analyzed by Google, but is perhaps richerin the diversity and types of links. From it can be mined a wealth ofinferences and insights, which can help inform what a particular user islikely to want done with a particular snapped image.

Artificial intelligence techniques can be applied to the data-miningtask. One class of such techniques is natural language processing (NLP),a science that has made significant advancements recently.

One example is the Semantic Map compiled by Cognition Technologies,Inc., a database that can be used to analyze words in context, in orderto discern their meaning. This functionality can be used, e.g., toresolve homonym ambiguity in analysis of image metadata (e.g., does“bow” refer to a part of a ship, a ribbon adornment, a performer'sthank-you, or a complement to an arrow? Proximity to terms such as“Carnival cruise,” “satin,” “Carnegie Hall” or “hunting” can provide thelikely answer). U.S. Pat. No. 5,794,050 details the underlyingtechnologies.

The understanding of meaning gained through NLP techniques can also beused to augment image metadata with other relevant descriptors—which canbe used as additional metadata in the embodiments detailed herein. Forexample, a close-up image tagged with the descriptor “hibiscus stamens”can—through NLP techniques—be further tagged with the term “flower.” (Asof this writing, Flickr has 338 images tagged with “hibiscus” and“stamen,” but omitting “flower.”)

U.S. Pat. No. 7,383,169 details how dictionaries and other large worksof language can be processed by NLP techniques to compile lexicalknowledge bases that serve as formidable sources of such “common sense”information about the world. This common sense knowledge can be appliedin the metadata processing detailed herein. (Wikipedia is anotherreference source that can serve as the basis for such a knowledge base.Our digital life log is yet another—one that yields insights unique tous as individuals.)

When applied to our digital life log, NLP techniques can reach nuancedunderstandings about our historical interests and actions—informationthat can be used to model (predict) our present interests andforthcoming actions. This understanding can be used to dynamicallydecide what information should be presented, or what action should beundertaken, responsive to a particular user capturing a particular image(or to other stimulus). Truly intuitive computing will then havearrived.

Other Comments

While the image/metadata processing detailed above takes many words todescribe, it need not take much time to perform. Indeed, much of theprocessing of reference data, compilation of glossaries, etc., can bedone off-line—before any input image is presented to the system. Flickr,Yahoo! or other service providers, can periodically compile andpre-process reference sets of data for various locales, to be quicklyavailable when needed to respond to an image query.

In some embodiments, other processing activities will be started inparallel with those detailed. For example, if initial processing of thefirst set of reference images suggests that the snapped image isplace-centric, the system can request likely-useful information fromother resources before processing of the user image is finished. Toillustrate, the system may immediately request a street map of thenearby area, together with a satellite view, a street view, a masstransit map, etc. Likewise, a page of information about nearbyrestaurants can be compiled, together with another page detailing nearbymovies and show-times, and a further page with a local weather forecast.These can all be sent to the user's phone and cached for later display(e.g., by scrolling a thumb wheel on the side of the phone).

These actions can likewise be undertaken before any image processingoccurs—simply based on the geocode data accompanying the cell phoneimage.

While geocoding data accompanying the cell phone image was used in thearrangement particularly described, this is not necessary. Otherembodiments can select sets of reference images based on other criteria,such as image similarity. (This may be determined by various metrics, asindicated above. Known image classification techniques can also be usedto determine one of several classes of images into which the input imagefalls, so that similarly-classed images can then be retrieved.) Anothercriteria is the IP address from which the input image is uploaded. Otherimages uploaded from the same—or geographically-proximate—IP addresses,can be sampled to form the reference sets.

Even in the absence of geocode data for the input image, the referencesets of imagery may nonetheless be compiled based on location. Locationinformation for the input image can be inferred from various indirecttechniques. The wireless service provider thru which the cell phoneimage is relayed may identify the particular cell tower from which thetourist's transmission was received. (If the transmission originatedthrough another wireless link, such as WiFi, its location may also beknown.) The tourist may have used his credit card an hour earlier at aManhattan hotel, allowing the system (with appropriate privacysafeguards) to infer that the picture was taken somewhere nearManhattan. Sometimes features depicted in the image are so iconic that aquick search for similar images in Flickr can locate the user (e.g., asbeing at the Eiffel Tower, or at the Statue of Liberty).

GeoPlanet was cited as one source of geographic information. However, anumber of other geoinformation databases can alternatively be used.GeoNames-dot-org is one. (It will be recognized that the “-dot-”convention, and omission of the usual http preamble, is used to preventthe reproduction of this text by the Patent Office from being indicatedas a live hyperlink). In addition to providing place names for a givenlatitude/longitude (at levels of neighborhood, city, state, country),and providing parent, child, and sibling information for geographicdivisions, its free data (available as a web service) also providesfunctions such as finding the nearest intersection, finding the nearestpost office, finding the surface elevation, etc.

It will be recognized that archives of aerial imagery are growingexponentially. Part of such imagery is from a straight-down perspective,but off-axis the imagery increasingly becomes oblique. From two or moredifferent oblique views of a location, a 3D model can be created. As theresolution of such imagery increases, sufficiently rich sets of data areavailable that—for some locations—a view of a scene as if taken fromground level may be synthesized. Such views can be matched with streetlevel photos, and metadata from one can augment metadata for the other.

As shown in FIG. 27, the embodiment particularly described above madeuse of various resources, including Flickr, a database of person names,a word frequency database, etc. These are just a few of the manydifferent information sources that might be employed in sucharrangements. Other social networking sites, shopping sites (e.g.,Amazon, EBay), weather and traffic sites, online thesauruses, caches ofrecently-visited web pages, browsing history, cookie collections,Google, other digital repositories (as detailed herein), etc., can allprovide a wealth of additional information that can be applied to theintended tasks. Some of this data reveals information about the user'sinterests, habits and preferences—data that can be used to better inferthe contents of the snapped picture, and to better tailor the intuitedresponse(s).

Likewise, while FIG. 27 shows a few lines interconnecting the differentitems, these are illustrative only. Different interconnections cannaturally be employed.

The arrangements detailed in this specification are a particular few outof myriad that may be employed. Most embodiments will be different thanthe ones detailed. Some actions will be omitted, some will performed indifferent orders, some will be performed in parallel rather thanserially (and vice versa), some additional actions may be included, etc.

One additional action is to refine the just-detailed process byreceiving user-related input, e.g., after the processing of the firstset of Flickr images. For example, the system identified “RockefellerCenter,” “Prometheus,” and “Skating rink” as relevant metadata to theuser-snapped image. The system may query the user as to which of theseterms is most relevant (or least relevant) to his/her particularinterest. The further processing (e.g., further search, etc.) can befocused accordingly.

Within an image presented on a touch screen, the user may touch a regionto indicate an object of particular relevance within the image frame.Image analysis and subsequent acts can then focus on the identifiedobject.

Some of the database searches can be iterative/recursive. For example,results from one database search can be combined with the originalsearch inputs and used as inputs for a further search.

It will be recognized that much of the foregoing processing is fuzzy.Many of the data is in terms of metrics that have no absolute meaning,but are relevant only to the extent different from other metrics. Manysuch different probabilistic factors can be assessed and then combined—astatistical stew. Artisans will recognize that the particularimplementation suitable for a given situation may be largely arbitrary.However, thru experience and Bayesian techniques, more informed mannersof weighting and using the different factors can be identified andeventually used.

If the Flickr archive is large enough, the first set of images in thearrangement detailed above may be selectively chosen to more likely besimilar to the subject image. For example, Flickr can be searched forimages taken at about the same time of day. Lighting conditions will beroughly similar, e.g., so that matching a night scene to a daylightscene is avoided, and shadow/shading conditions might be similar.Likewise, Flickr can be searched for images taken in the sameseason/month. Issues such as seasonal disappearance of the ice skatingrink at Rockefeller Center, and snow on a winter landscape, can thus bemitigated. Similarly, if the camera/phone is equipped with technologypermitting its bearing (and/or azimuth/elevation) to be determined, thenFlickr can be searched for shots with this degree of similarity too.

Moreover, the sets of reference images collected from Flickr desirablycomprise images from many different sources (photographers)—so theydon't tend towards use of the same metadata descriptors.

Images collected from Flickr may be screened for adequate metadata. Forexample, images with no metadata (except, perhaps, an arbitrary imagenumber) may be removed from the reference set(s). Likewise, images withless than 2 (or 20) metadata terms, or without a narrative description,may be disregarded.

Flickr is often mentioned in this specification, but other collectionsof content can of course be used. Images in Flickr commonly havespecified license rights for each image. These include “all rightsreserved,” as well as a variety of Creative Commons licenses, throughwhich the public can make use of the imagery on different terms. Systemsdetailed herein can limit their searches through Flickr for imagerymeeting specified license criteria (e.g., disregard images marked “allrights reserved”).

Other image collections are in some respect preferable. For example, thedatabase at images.google-dot-com seems better at ranking images basedon metadata-relevance than Flickr.

Flickr and Google maintain image archives that are publicly accessible.Many other image archives are private. The present technology findsapplication with both—including some hybrid contexts in which bothpublic and proprietary image collections are used (e.g., Flickr is usedto find an image based on a user image, and the Flickr image issubmitted to a private database to find a match and determine acorresponding response for the user).

Similarly, while reference was made to services such as Flickr forproviding data (e.g., images and metadata), other sources can of coursebe used.

One alternative source is an ad hoc peer-to-peer (P2P) network. In sucha P2P arrangement, there may optionally be a central index, with whichpeers can communicate in searching for desired content, and detailingthe content they have available for sharing. The index may includemetadata and metrics for images, together with pointers to the nodes atwhich the images themselves are stored.

The peers may include cameras, PDAs, and other portable devices, fromwhich image information may be available nearly instantly after it hasbeen captured.

In the course of the methods detailed herein, certain relationships arediscovered between imagery (e.g., similar geolocation; similar imagemetrics; similar metadata, etc). These data are generally reciprocal, soif the system discovers—during processing of Image A, that its colorhistogram is similar to that of Image B, then this information can bestored for later use. If a later process involves Image B, theearlier-stored information can be consulted to discover that Image A hasa similar histogram—without analyzing Image A. Such relationships areakin to virtual links between the images.

For such relationship information to maintain its utility over time, itis desirable that the images be identified in a persistent manner. If arelationship is discovered while Image A is on a user's PDA, and Image Bis on a desktop somewhere, a means should be provided to identify ImageA even after it has been transferred to the user's MySpace account, andto track Image B after it has been archived to an anonymous computer ina cloud network.

Images can be assigned Digital Object Identifiers (DOI) for thispurpose. The International DOI Foundation has implemented the CNRIHandle System so that such resources can be resolved to their currentlocation through the web site at doi-dot-org.

If several different repositories are being searched for imagery orother information, it is often desirable to adapt the query to theparticular databases being used. For example, different facialrecognition databases may use different facial recognition parameters.To search across multiple databases, technologies such as detailed inpatent applications 20040243567 and 20060020630 can be employed toensure that each database is probed with an appropriately-tailoredquery.

Frequent reference has been made to images, but in many cases otherinformation may be used in lieu of image information itself. Indifferent applications image identifiers, characterizing eigenvalues,associated metadata, decoded barcode or watermark data, etc., may beused instead of imagery, per se (e.g., as a data proxy).

While the example just-given spoke of geocoding by latitude/longitudedata, in other arrangements the cell phone/camera may provide locationdata in one or more other reference systems, such as Yahoo's GeoPlanetID—the Where on Earth ID (WOEID).)

Location metadata can be used for identifying other resources inaddition to similarly-located imagery. Web pages, for example, can havegeographical associations (e.g., a blog may concern the author'sneighborhood; a restaurant's web page is associated with a particularphysical address). The web service GeoURL-dot-org is a location-to-URLreverse directory that can be used to identify web sites associated withparticular geographies.

GeoURL supports a variety of location tags, including their own ICMBmeta tags, as well as Geo Tags. Other systems that support geotagginginclude RDF, Geo microformat, and the GPSLongitude/GPSLatitude tagscommonly used in XMP- and EXIF-camera metainformation. Flickr uses asyntax established by Geobloggers, e.g.

geotagged

geo:lat=57.64911

geo:lon=10.40744

In processing metadata, it is sometimes helpful to clean-up the dataprior to analysis, as referenced above. The metadata may also beexamined for dominant language, and if not English (or other particularlanguage of the implementation), the metadata and the associated imagemay be removed from consideration.

While the just-detailed embodiment sought to identify the image subjectas being one of a person/place/thing so that a correspondingly-differentaction can be taken, analysis/identification of the image within otherclasses can naturally be employed. A few examples of the countless otherclass/type groupings include animal/vegetable/mineral;golf/tennis/football/baseball; male/female;wedding-ring-detected/wedding-ring-not-detected; urban/rural;rainy/clear; day/night; child/adult; summer/autumn/winter/spring;car/truck; consumer product/non-consumer product; can/box/bag;natural/man-made; suitable for all ages/parental advisory for children13 and below/parental advisory for children 17 and below/adult only;etc.

Sometimes different analysis engines may be applied to the user's imagedata. These engines can operate sequentially, or in parallel. Forexample, FIG. 28A shows an arrangement in which—if an image isidentified as person-centric—it is next referred to two other engines.One identifies the person as family, friend or stranger. The otheridentifies the person as child or adult. The latter two engines work inparallel, after the first has completed its work.

Sometimes engines can be employed without any certainty that they areapplicable. For example, FIG. 28B shows engines performingfamily/friend/stranger and child/adult analyses—at the same time theperson/place/thing engine is undertaking its analysis. If the latterengine determines the image is likely a place or thing, the results ofthe first two engines will likely not be used.

(Some existing online services can be used for certain types of imagediscrimination/identification. For example, when an image of an aircraftis uploaded to one online site, it returns an identification of theplane by make and model. The arrangements detailed herein can referimagery that appears to be of aircraft to such a site, and use thereturned identification information. Or all input imagery can bereferred to such a site; most of the returned results will be ambiguousand will not be used.)

FIG. 29 shows that different analysis engines may provide their outputsto different response engines. Often the different analysis engines andresponse engines may be operated by different service providers. Theoutputs from these response engines can then be consolidated/coordinatedfor presentation to the consumer. (This consolidation may be performedby the user's cell phone—assembling inputs from different data sources.)

One example of the technology detailed herein is a homebuilder who takesa cell phone image of a drill that needs a spare part. The image isanalyzed, the drill is identified by the system as a Black and DeckerDR250B, and the user is provided various info/action options. Theseinclude reviewing photos of drills with similar appearance, reviewingphotos of drills with similar descriptors/features, reviewing the user'smanual for the drill, seeing a parts list for the drill, buying thedrill new from Amazon or used from EBay, listing the builder's drill onEBay, buying parts for the drill, etc. The builder chooses the “buyingparts” option and proceeds to order the necessary part. (FIG. 21.)

Another example is a person shopping for a home. She snaps a photo ofthe house. The system refers the image both to a private database of MLSinformation, and a public database such as Google. The system respondswith a variety of options, including reviewing photos of the nearesthouses offered for sale; reviewing photos of houses listed for sale thatare closest in value to the pictured home, and within the same zip-code;reviewing photos of houses listed for sale that are most similar infeatures to the pictured home, and within the same zip-code;neighborhood and school information, etc. (FIG. 23.)

In another example, a first user snaps an image of Paul Simon at aconcert. The system automatically posts the image to the user's Flickraccount—together with metadata inferred by the procedures detailedabove. (The name of the artist may have been found in a search of Googlefor the user's geolocation; e.g., a Ticketmaster web page revealed thatPaul Simon was playing that venue that night.) The first user's picture,a moment later, is encountered by a system processing a secondconcert-goer's photo of the same event, from a different vantage. Thesecond user is shown the first user's photo as one of the system'sresponses to the second photo. The system may also alert the first userthat another picture of the same event—from a different viewpoint—isavailable for review on his cell phone, if he'll press a certain buttontwice.

In many such arrangements, it will be recognized that “the content isthe network.” Associated with each photo, or each subject depicted in aphoto (or any other item of digital content or information expressedtherein), is a set of data and attributes that serve as implicit- orexpress-links to actions and other content. The user can navigate fromone to the next—navigating between nodes on a network.

Television shows are rated by the number of viewers, and academic papersare judged by the number of later citations. Abstracted to a higherlevel, it will be recognized that such “audience measurement” forphysical- or virtual-content is the census of links that associate itwith other physical- or virtual-content.

While Google is limited to analysis and exploitation of links betweendigital content, the technology detailed herein allows the analysis andexploitation of links between physical content as well (and betweenphysical and electronic content).

Known cell phone cameras and other imaging devices typically have asingle “shutter” button. However, the device may be provided withdifferent actuator buttons—each invoking a different operation with thecaptured image information. By this arrangement, the user canindicate—at the outset—the type of action intended (e.g., identify facesin image per Picasa or VideoSurf information, and post to my FaceBookpage; or try and identify the depicted person, and send a “friendrequest” to that person's MySpace account).

Rather than multiple actuator buttons, the function of a sole actuatorbutton might be controlled in accordance with other UI controls on thedevice. For example, repeated pressing of a Function Select button cancause different intended operations to be displayed on the screen of theUI. When the user then presses the shutter button, the selectedoperation is invoked.

One common response (which may need no confirmation) is to post theimage on Flickr or social network site(s). Metadata inferred by theprocesses detailed herein can be saved in conjunction with the imagery(qualified, perhaps, as to its confidence).

In the past, the “click” of a mouse served to trigger a user-desiredaction. That action identified an X- Y-location coordinate on a virtuallandscape (e.g., a desktop screen) that indicated the user's expressintention. Going forward, this role will increasingly be served by the“snap” of a shutter—capturing a real landscape from which a user'sintention will be inferred.

Business rules can dictate a response appropriate to a given situation.These rules and responses may be determined by reference to datacollected by web indexers, such as Google, etc., using intelligentrouting.

Crowdsourcing is not suitable for real-time implementations. However,inputs that stymie the system and fail to yield a corresponding action(or yield actions from which user selects none) can be referred offlinefor crowdsource analysis—so that next time it's presented, it can behandled better.

Image-based navigation systems present a different topology than isfamiliar from web page-based navigation system. FIG. 17A shows that webpages on the internet relate in a point-to-point fashion. For example,web page 1 may link to web pages 2 and 3. Web page 3 may link to page 2.Web page 2 may link to page 4. Etc. FIG. 17B shows the contrastingnetwork associated with image-based navigation. The individual imagesare linked a central node (e.g., a router), which then links to furthernodes (e.g., response engines) in accordance with the image information.

The “router” here does not simply route an input packet to a destinationdetermined by address information conveyed with the packet—as in thefamiliar case with internet traffic routers. Rather, the router takesimage information and decides what to do with it, e.g., to whichresponsive system should the image information be referred.

Routers can be stand-alone nodes on a network, or they can be integratedwith other devices. (Or their functionality can be distributed betweensuch locations.) A wearable computer may have a router portion (e.g., aset of software instructions)—which takes image information from thecomputer, and decides how it should be handled. (For example, if itrecognizes the image information as being an image of a business card,it may OCR name, phone number, and other data, and enter it into acontacts database.) The particular response for different types of inputimage information can be determined by a registry database, e.g., of thesort maintained by a computer's operating system.

Likewise, while response engines can be stand-alone nodes on a network,they can also be integrated with other devices (or their functionsdistributed). A wearable computer may have one or several differentresponse engines that take action on information provided by the routerportion.

FIG. 32 shows an arrangement employing several computers (A-E), some ofwhich may be wearable computers (e.g., cell phones). The computersinclude the usual complement of processor, memory, storage,input/output, etc. The storage or memory can contain content, such asimages, audio and video. The computers can also include one or morerouters and/or response engines. Standalone routers and response enginesmay also be coupled to the network

The computers are networked, shown schematically by link 50. Thisconnection can be by any known networking arrangement, including theinternet and/or wireless links (WiFi, WiMax, Bluetooth, etc), Softwarein at least certain of the computers includes a peer-to-peer (P2P)client, which makes that computer's resources available to othercomputers on the network, and reciprocally enables that computer toemploy resources of the other computers.

Though the P2P client, computer A may obtain image, video and audiocontent from computer B. Sharing parameters on computer B can be set todetermine which content is shared, and with whom. Data on computer B mayspecify, for example, that some content is to be kept private; some maybe shared with known parties (e.g., a tier of social network “Friends”);and other may be freely shared. (Other information, such as geographicposition information, may also be shared—subject to such parameters.)

In addition to setting sharing parameters based on party, the sharingparameters may also specify sharing based on the content age. Forexample, content/information older than a year might be shared freely,and content older than a month might be shared with a tier of friends.In other arrangements, fresher content might be the type most liberallyshared. E.g., content captured or stored within the past hour, day orweek might be shared freely, and content from within the past month oryear might be shared with friends.

An exception list can identify content—or one or more classes ofcontent—that is treated differently than the above-detailed rules (e.g.,never shared or always shared).

In addition to sharing content, the computers can also share theirrespective router and response engine resources across the network.Thus, for example, if computer A does not have a response enginesuitable for a certain type of image information, it can pass theinformation to computer B for handling by its response engine.

It will be recognized that such a distributed architecture has a numberof advantages, in terms of reduced cost increased reliability.Additionally, the “peer” groupings can be defined geographically, e.g.,computers that find themselves within a particular spatial environment(e.g., an area served by a particular WiFi system). The peers can thusestablish dynamic, ad hoc subscriptions to content and services fromnearby computers. When the computer leaves that environment, the sessionends.

Some researchers foresee the day when all of our experiences arecaptured in digital form. Indeed, Gordon Bell at Microsoft has compileda digital archive of his recent existence through his technologiesCyberAll, SenseCam and MyLifeBits. Included Bell's archive arerecordings of all telephone calls, video of daily life, captures of allTV and radio consumed, archive of all web pages visited, map data of allplaces visited, polysomnograms for his sleep apnea, etc., etc., etc.(For further information see, e.g., at Bell, A Digital Life, ScientificAmerican, March, 2007; Gemmell, MyLifeBits: a personal database foreverything, Microsoft Research Technical Report MSR-TR-2006-23; Gemmell,Passive Capture and Ensuing Issues for a Personal Lifetime Store,Proceedings of The First ACM Workshop on Continuous Archival andRetrieval of Personal Experiences (CARPE '04), pp. 48-55; Wilkinson,Remember This, The New Yorker, May 27, 2007. See also the otherreferences cited at Gordon's Bell's Microsoft Research web page, and theACM Special Interest Group web page for CARPE (Capture, Archival &Retrieval of Personal Experiences) web page.)

The present technology is well suited for use with such experientialdigital content—either as input to a system (i.e., the system respondsto the user's present experience), or as a resource from which metadata,habits, and other attributes can be mined (including service in the roleof the Flickr archive in the embodiments earlier detailed).

In embodiments that employ personal experience as an input, it isinitially desirable to have the system trigger and respond only whendesired by the user—rather than being constantly free-running (which iscurrently prohibitive from the standpoint of processing, memory andbandwidth issues).

The user's desire can be expressed by a deliberate action by the user,e.g., pushing a button, or making a gesture with head or hand. Thesystem takes data from the current experiential environment, andprovides candidate responses.

More interesting, perhaps, are systems that determine the user'sinterest through biological sensors. Electroencephalography, forexample, can be used to generate a signal that triggers the system'sresponse (or triggers one of several different responses, e.g.,responsive to different stimuli in the current environment). Skinconductivity, pupil dilation, and other autonomous physiologicalresponses can also be optically or electrically sensed, and provide atriggering signal to the system.

Eye tracking technology can be employed to identify which object in afield of view captured by an experiential-video sensor is of interest tothe user. If Tony is sitting in a bar, and his eye falls on a bottle ofunusual beer in front of a nearby woman, the system can identify hispoint of focal attention, and focus its own processing efforts on pixelscorresponding to that bottle. With a signal from Tony, such as two quickeye-blinks, the system can launch an effort to provide candidateresponses based on that beer bottle—perhaps also informed by otherinformation gleaned from the environment (time of day, date, ambientaudio, etc.) as well as Tony's own personal profile data. (Gazerecognition and related technology is disclosed, e.g., in patentpublication 20080211766.)

The system may quickly identify the beer as Doppelbock, e.g., by patternmatching from the image (and/or OCR). With that identifier it findsother resources indicating the beer originates from Bavaria, where it isbrewed by monks of St. Francis of Paula. Its 9% alcohol content also isdistinctive.

By checking personal experiential archives that friends have madeavailable to Tony, the system learns that his buddy Geoff is fond ofDoppelbock, and most recently drank a bottle in a pub in Dublin. Tony'sglancing encounter with the bottle is logged in his own experientialarchive, where Geoff may later encounter same. The fact of the encountermay also be real-time-relayed to Geoff in Prague, helping populate anon-going data feed about his friends' activities.

The bar may also provide an experiential data server, to which Tony iswirelessly granted access. The server maintains an archive of digitaldata captured in the bar, and contributed by patrons. The server mayalso be primed with related metadata & information the management mightconsider of interest to its patrons, such as the Wikipedia page on thebrewing methods of the monks of St Paul, what bands might be playing inweeks to come or what the night's specials are. (Per user preference,some users require that their data be cleared when they leave the bar;others permit the data to be retained.) Tony's system may routinelycheck the local environment's experiential data server to see what oddbits of information might be found. This time it shows that the woman atbarstool 3 (who might employ a range privacy heuristics to know whereand with whom to share her information, in this example she might screenher identity from strangers)—the woman with the Doppelbock—has, amongher friends, a Tom <last name encrypted>. Tony's system recognizes thatGeoff's circle of friends (which Geoff makes available to his friends)includes the same Tom.

A few seconds after his double-blink, Tony's cell phone vibrates on hisbelt. Flipping it open and turning the scroll wheel on the side, Tonyreviews a series of screens on which the system presents information ithas gathered—with the information it deems most useful to Tony shownfirst.

Equipped with knowledge about this Tony-Geoff-Tom connection (closerthan the usual six-degrees-of-separation), and primed with trivia abouther Doppelbock beer, Tony picks up his glass and walks down the bar.

While P2P networks such as BitTorrent have permitted sharing of audio,image and video content, arrangements like that shown in FIG. 32 allownetworks to share a contextually-richer set of experiential content. Abasic tenet of P2P networks is that even in the face of technologiesthat that mine the long-tail of content, the vast majority of users areinterested in similar content (the score of tonight's NBA game, thecurrent episode of Lost, etc.) and that given sufficient bandwidth andprotocols, the most efficient mechanism to deliver similar content tousers not by sending individual streams, but by piecing the contenttogether based on what your “neighbors” have on the network. This samemechanism can be used to provide metadata related to enhancing anexperience such as being at the bar drinking a Dopplebock, or watching ahighlight of tonight's NBA game on a phone while at the bar. Theprotocol used in the ad-hoc network described above, might leverage P2Pprotocols with the experience server providing a peer registrationservice (similar to early P2P networks) or in a true P2P modality, withall devices in the ad-hoc network advertising what experiences(metadata, content, social connections, etc.) they have available(either for free or for barter of information in-kind, etc.)

Within this fabric, Tony's cell phone may simply retrieve theinformation on Dopplebock by posting the question to the peer networkand receive a wealth of information from a variety of devices within thebar or the experience server, without ever knowing the source.Similarly, the experience server may also act as data-recorder,recording the experiences of those within the ad-hoc network, providinga persistence to experience in time & place. Geoff may visit the samebar at some point in the future and see what threads of communication orconnections his friend Tony made two weeks earlier, or possibly evenleave a note for Tony to retrieve a future time next time he is at thebar.

The ability to mine the social threads represented by the traffic on thenetwork, can also enable the proprietors of the bar to augment theexperiences of the patrons by orchestrating interaction orintroductions. This may include people with shared interests, singles,etc. or in the form of gaming by allowing people to opt-in to themebased games, where patrons piece together clues to find the trueidentity of someone in the bar or unravel a mystery (similar to theboard game Clue). Finally, the demographic information as it relates toaudience measurement is of material value to proprietors as theyconsider which beers to stock next, where to advertise, etc.

Still Further Discussion

Certain portable devices, such as the Apple iPhone, offer single-buttonaccess to pre-defined functions. Among these are viewing prices offavorite stocks, viewing a weather forecast, and viewing a general mapof the user's location. Additional functions are available, but the usermust undertake a series of additional manipulations, e.g., to reach afavorite web site, etc.

An embodiment of the present technology allows these furthermanipulations to be shortcut by use of certain image snapshots. Snappinga photo of the user's hand can link the user to a babycam backhome—delivering real time video of a newborn in a crib. Snapping a photoof a wristwatch can load a map showing traffic conditions along somepart of a route on the user's drive home, etc. Such functionality isshown in FIGS. 33-35.

A user interface for the portable device includes a set-up/trainingphase that allows the user to associate different functions withdifferent visual signs. The user is prompted to snap a picture, andenter the URL and name of an action that is to be associated with thatpicture. (The URL is one type of response; others can also be used—suchas launching a JAVA application, etc.)

The system then characterizes the snapped image by deriving a set offeature vectors by which similar snapshots can be recognized (e.g., thrupattern/template matching). The feature vectors are stored in a datastructure (FIG. 35), in association with the function name andassociated URL.

In this initial training phase, the user may snap several photos of thesame visual sign—perhaps from different distances and perspectives, andwith different lighting and backgrounds. The feature extractionalgorithm processes the collection to extract a feature set thatcaptures shared similarities of all of the training images.

The extraction of image features, and storage of the data structure, canbe performed at the portable device, or at a remote device (or indistributed fashion).

In later operation, the device can check each image captured by thedevice for correspondence with one of the stored visual signs. If any isrecognized, the corresponding action can be launched. Else, the deviceresponds with the other functions available to the user upon capturing anew image.

In another embodiment, the portable device is equipped with two or moreshutter buttons. Manipulation of one button captures an image andexecutes an action—based on a closest match between the captured imageand a stored visual sign. Manipulation of another button captures animage without undertaking such an action.

The device UI can include a control that presents a visual glossary ofsigns to the user, as shown in FIG. 34. When activated, thumbnails ofdifferent visual signs are presented on the device display, inassociation with names of the functions earlier stored—reminding theuser of the defined vocabulary of signs.

The control that launches this glossary of signs can—itself—be an image.One image suitable for this function is a generally featureless frame.An all-dark frame can be achieved by operating the shutter with the lenscovered. An all-light frame can be achieved by operating the shutterwith the lens pointing at a light source. Another substantiallyfeatureless frame (of intermediate density) may be achieved by imaging apatch of skin, or wall, or sky. (To be substantially featureless, theframe should be closer to featureless than matching one of the otherstored visual signs. In other embodiments, “featureless” can beconcluded if the image has a texture metric below a threshold value.)

(The concept of triggering an operation by capturing an all-light framecan be extended to any device function. In some embodiments, repeatedall-light exposures alternatively toggle the function on and off.Likewise with all-dark and intermediate density frames. A threshold canbe set—by the user with a UI control, or by the manufacturer—toestablish how “light” or “dark” such a frame must be in order to beinterpreted as a command. For example, 8-bit (0-255) pixel values from amillion pixel sensor can be summed. If the sum is less than 900,000, theframe may be regarded as all-dark. If greater than 254 million, theframe may be regarded as all-light. Etc.)

One of the other featureless frames can trigger another specialresponse. It can cause the portable device to launch all of the storedfunctions/URLs (or, e.g., a certain five or ten) in the glossary. Thedevice can cache the resulting frames of information, and present themsuccessively when the user operates one of the phone controls, such asbutton 16 b or scroll wheel 24 in FIG. 24, or makes a certain gesture ona touch screen. (This function can be invoked by other controls aswell.)

The third of the featureless frames (i.e., dark, white, or mid-density)can send the device's location to a map server, which can then transmitback multiple map views of the user's location. These views may includeaerial views and street map views at different zoom levels, togetherwith nearby street-level imagery. Each of these frames can be cached atthe device, and quickly reviewed by turning a scroll wheel or other UIcontrol.

The user interface desirably includes controls for deleting visualsigns, and editing the name/functionality assigned to each. The URLs canbe defined by typing on a keypad, or by navigating otherwise to adesired destination and then saving that destination as the responsecorresponding to a particular image.

Training of the pattern recognition engine can continue through use,with successive images of the different visual signs each serving torefine the template model by which that visual sign is defined.

It will be recognized that a great variety of different visual signs canbe defined, using resources that are commonly available to the user. Ahand can define many different signs, with fingers arranged in differentpositions (fist, one- through five-fingers, thumb-forefinger OK sign,open palm, thumbs-up, American sign language signs, etc). Apparel andits components (e.g., shoes, buttons) can also be used, as can jewelry.Features from common surroundings (e.g., telephone) may also be used.

In addition to launching a particular favorite operation, suchtechniques can be used as a user interface technique in othersituations. For example, a software program or web service may present alist of options to the user. Rather than manipulating a keyboard toenter, e.g., choice #3, the user may snap a picture of threefingers—visually symbolizing the selection. Software recognizes thethree finger symbol as meaning the digit 3, and inputs that value to theprocess.

If desired, visual signs can form part of authentication procedures,e.g., to access a bank or social-networking web site. For example, afterentering a sign-on name or password at a site, the user may be shown astored image (to confirm that the site is authentic) and then beprompted to submit an image of a particular visual type (earlier definedby the user, but not now specifically prompted by the site). The website checks features extracted from the just-captured image forcorrespondence with an expected response, before permitting the user toaccess the web site.

Other embodiments can respond to a sequence of snapshots within acertain period (e.g., 10 seconds)—a grammar of imagery. An imagesequence of “wristwatch,” “four fingers” “three fingers” can set analarm clock function on the portable device to chime at 7 am.

In still other embodiments, the visual signs may be gestures thatinclude motion—captured as a sequence of frames (e.g., video) by theportable device.

Context data (e.g., indicating the user's geographic location, time ofday, month, etc.) can also be used to tailor the response. For example,when a user is at work, the response to a certain visual sign may be tofetch to an image from a security camera from the user's home. At work,the response to the same sign may be to fetch an image from a securitycamera at work.

In this embodiment, as in others, the response needn't be visual. Audioor other output (e.g., tactile, smell, etc.) can of course be employed.

The just-described technology allows a user to define a glossary ofvisual signs and corresponding customized responses. An intendedresponse can be quickly invoked by photographing a readily-availablesubject. The captured image can be of low quality (e.g., overexposed,blurry), since it only needs to be classified among, and distinguishedfrom, a relatively small universe of alternatives.

Visual Intelligence Pre-Processing

Another aspect of the present technology is to perform one or morevisual intelligence pre-processing operations on image informationcaptured by a camera sensor. These operations may be performed withoutuser request, and before other image processing operations that thecamera customarily performs.

FIG. 36 is a simplified diagram showing certain of the processingperformed in an exemplary camera, such as a cell phone camera. Lightimpinges on an image sensor comprising an array of photodiodes. (CCD orCMOS sensor technologies are commonly used.) The resulting analogelectrical signals are amplified, and converted to digital form by D/Aconverters. The outputs of these D/A converters provide image data inits most raw, or “native,” form.

The foregoing operations are typically performed by circuitry formed ona common substrate, i.e., “on-chip.” Before other processes can accessthe image data, one or more other processes are commonly performed.

One such further operation is Bayer interpolation (de-mosaicing). Thephotodiodes of the sensor array typically each captures only a singlecolor of light: red, green or blue (R/G/B), due to a color filter array.This array is comprised of a tiled 2×2 pattern of filter elements: onered, a diagonally-opposite one blue, and the other two green. Bayerinterpolation effectively “fills in the blanks” of the sensor'sresulting R/G/B mosaic pattern, e.g., providing a red signal where thereis a blue filter, etc.

Another common operation is white balance correction. This processadjusts the intensities of the component R/G/B colors in order to rendercertain colors (especially neutral colors) correctly.

Other operations that may be performed include gamma correction and edgeenhancement.

Finally, the processed image data is typically compressed to reducestorage requirements. JPEG compression is most commonly used.

The processed, compressed image data is then stored in a buffer memory.Only at this point is the image information commonly available to otherprocesses and services of the cell phone (e.g., by calling a systemAPI).

One such process that is commonly invoked with this processed image datais to present the image to the user on the screen of the camera. Theuser can then assess the image and decide, e.g., whether (1) to save itto the camera's memory card, (2) to transmit it in a picture message,(3) to delete it, etc.

Until the user instructs the camera (e.g., through a control in agraphical or button-based user interface), the image stays in the buffermemory. Without further instruction, the only use made of the processedimage data is to display same on the screen of the cell phone.

FIG. 37 shows an exemplary embodiment of the presently-discussed aspectof the technology. After converting the analog signals into digitalnative form, one or more other processes are performed.

One such process is to perform a Fourier transformation on the nativeimage data. This converts the spatial-domain representation of the imageinto a frequency-domain representation.

A Fourier-domain representation of the native image data can be usefulin various ways. One is to screen the image for likely barcode data.

As is familiar, a 2D barcode is a checkerboard-like array of light- anddark-squares. The size of the component squares, and thus theirrepetition spacing, gives a pair of notable peaks in the Fourier-domainrepresentation of the image at a corresponding frequency. (The peaks maybe phase-spaced ninety degrees in the UV plane, if the pattern recurs inequal frequency in both the vertical and horizontal directions.) Thesepeaks extend significantly above other image components at nearby imagefrequencies—with the peaks often having a magnitude twice- to five- orten-times (or more) that of nearby image frequencies. If the Fouriertransformation is done on tiled patches from the image (e.g., patches of16×16 pixels, or 128×128 pixels, etc), it may be found that certainpatches that are wholly within a barcode portion of the image frame haveessentially no signal energy except at this characteristic frequency.

As shown in FIG. 37, Fourier transform information can be analyzed fortelltale signs associated with an image of a barcode. A template-likeapproach can be used. The template can comprise a set of parametersagainst which the Fourier transform information is tested—to see if thedata has indicia associated with a barcode-like pattern.

If the Fourier data is consistent with an image depicting a 2D barcode,corresponding information can be routed for further processing (e.g.,sent from the cell phone to a barcode-responsive service). Thisinformation can comprise the native image data, and/or the Fouriertransform information derived from the image data.

In the former case, the full image data needn't be sent. In someembodiments a down-sampled version of the image data, e.g., one-fourththe resolution in both the horizontal and vertical directions, can besent. Or just patches of the image data having the highest likelihood ofdepicting part of a barcode pattern can be sent. Or, conversely, patchesof the image data having the lowest likelihood of depicting a barcodecan not be sent. (These may be patches having no peak at thecharacteristic frequency, or having a lower amplitude there thannearby.)

The transmission can be prompted by the user. For example, the camera UImay ask the user if information should be directed for barcodeprocessing. In other arrangements, the transmission is dispatchedimmediately upon a determination that the image frame matches thetemplate, indicating possible barcode data. No user action is involved.

The Fourier transform data can be tested for signs of other imagesubjects as well. A 1D barcode, for example, is characterized by asignificant amplitude component at a high frequency—(going “across thepickets,” and another significant amplitude spike at a lowfrequency—going along the pickets. (Significant again means two-or-moretimes the amplitude of nearby frequencies, as noted above.) Other imagecontents can also be characterized by reference to their Fourier domainrepresentation, and corresponding templates can be devised. Fouriertransform data is also commonly used in computing fingerprints used forautomated recognition of media content.

The Fourier-Mellin (F-M) transform is also useful in characterizingvarious image subjects/components—including the barcodes noted above.The F-M transform has the advantage of being robust to scale androtation of the image subject (scale/rotation invariance). In anexemplary embodiment, if the scale of the subject increases (as bymoving the camera closer), the F-M transform pattern shifts up; if thescale decreases, the F-M pattern shifts down. Similarly, if the subjectis rotated clockwise, the F-M pattern shifts right; if rotatedcounter-clockwise, the F-M pattern shifts left. (The particulardirections of the shifts can be tailored depending on theimplementation.) These attributes make F-M data important in recognizingpatterns that may be affine-transformed, such as facial recognition,character recognition, object recognition, etc.

The arrangement shown in FIG. 37 applies a Mellin transform to theoutput of the Fourier transform process, to yield F-M data. The F-M canthen be screened for attributes associated with different imagesubjects.

For example, text is characterized by plural symbols of approximatelysimilar size, composed of strokes in a foreground color that contrastwith a larger background field. Vertical edges tend to dominate (albeitslightly inclined with italics), with significant energy also beingfound in the horizontal direction. Spacings between strokes usually fallwithin a fairly narrow range.

These attributes manifest themselves as characteristics that tend toreliably fall within certain boundaries in the F-M transform space.Again, a template can define tests by which the F-M data is screened toindicate the likely presence of text in the captured native image data.If the image is determined to include likely-text, it can be dispatchedto a service that handles this type of data (e.g., an optical characterrecognition, or OCR, engine). Again, the image (or a variant of theimage) can be sent, or the transform data can be sent.

Just as text manifests itself with a certain set of characteristicattributes in the F-M, so do faces. The F-M data output from the Mellintransform can be tested against a different template to determine thelikely presence of a face within the captured image.

Likewise, the F-M data can be examined for tell-tale signs that theimage data conveys a watermark. A watermark orientation signal is adistinctive signal present in some watermarks that can serve as a signthat a watermark is present.

In the examples just given, as in others, the templates may be compiledby testing with known images (e.g., “training”). By capturing images ofmany different text presentations, the resulting transform data can beexamined for attributes that are consistent across the sample set, or(more likely) that fall within bounded ranges. These attributes can thenbe used as the template by which images containing likely-text areidentified. (Likewise for faces, barcodes, and other types of imagesubjects.)

FIG. 37 shows that a variety of different transforms can be applied tothe image data. These are generally shown as being performed inparallel, although one or more can be performed sequentially—either alloperating on the same input image data, or one transform using an outputof a previous transform (as is the case with the Mellin transform).Although not all shown (for clarity of illustration), outputs from eachof the other transform processes can be examined for characteristicsthat suggest the presence of a certain image type. If found, relateddata is then sent to a service appropriate to that type of imageinformation.

In addition to Fourier transform and Mellin transform processes,processes such as eigenface (eigenvector) calculation, imagecompression, cropping, affine distortion, filtering, DCT transform,wavelet transform, Gabor transform, and other signal processingoperations can be applied (all are regarded as transforms). Others arenoted elsewhere in this specification. Outputs from these processes arethen tested for characteristics indicating that the chance the imagedepicts a certain class of information, is greater than a random chance.

The outputs from some processes may be input to other processes. Forexample, an output from one of the boxes labeled ETC in FIG. 37 isprovided as an input to the Fourier transform process. This ETC box canbe, for example, a filtering operation. Sample filtering operationsinclude median, Laplacian, Wiener, Sobel, high-pass, low-pass, bandpass,Gabor, signum, etc. (U.S. Pat. Nos. 6,442,284, 6,516,079, 6,614,914,6,988,202, 7,013,021 and 7,076,082 show various such filters.)

Sometimes a single service may handle different data types, or data thatpasses different screens. In FIG. 37, for example, a facial recognitionservice may receive F-M transform data, or eigenface data. Or it mayreceive image information that has passed one of several differentscreens (e.g., its F-M transform passed one screen, or its eigenfacerepresentation passed a different screen).

In some cases, data can be sent to two or more different services.

Although not essential, it is desirable that some or all of theprocessing shown in FIG. 37 be performed by circuitry integrated on thesame substrate as the image sensors. (Some of the operations may beperformed by programmable hardware—either on the substrate oroff—responsive to software instructions.)

While the foregoing operations are described as immediately followingconversion of the analog sensor signals to digital form, in otherembodiments such operations can be performed after other processingoperations (e.g., Bayer interpolation, white balance correction, JPEGcompression, etc.).

Some of the services to which information is sent may be providedlocally in the cell phone. Or they can be provided by a remote device,with which the cell phone establishes a link that is at least partlywireless. Or such processing can be distributed among various devices.

(While described in the context of conventional CCD and CMOS sensors,this technology is applicable regardless of sensor type. Thus, forexample, Foveon and panchromatic image sensors can alternately be used.So can high dynamic range sensors, and sensors using Kodak's TruesenseColor Filter Pattern (which add panchromatic sensor pixels to the usualBayer array of red/green/blue sensor pixels). Sensors with infraredoutput data can also advantageously be used. For example, infraredoutput data can be used to identify faces and other image subjects withtemperature differentials—aiding in segmenting image subjects within theframe.)

It will be recognized that devices employing the FIG. 37 architecturehave, essentially, two parallel processing chains. One processing chainproduces data to be rendered into perceptual form for use by humanviewers. This chain typically includes at least one of a de-mosaicprocessor and a JPEG image compressor, etc. The second processing chainproduces data to be analyzed by one or more machine-implementedalgorithms, and in the illustrative example includes a Fourier transformprocessor, an eigenface processor, etc.

By arrangements such as the foregoing, one or more appropriateimage-responsive services can begin formulating candidate responses tothe visual stimuli before the user has even decided what to do with thecaptured image.

Further Comments on Visual Intelligence Pre-Processing

While static image pre-processing was discussed in connection with FIG.37 (and FIG. 30), such processing can also include temporal aspects,such as motion.

Motion is most commonly associated with video, and the techniquesdetailed herein can be used when capturing video content. However,motion/temporal implications are also present with “still” imagery.

For example, some image sensors are read sequentially, top row to bottomrow. During the reading operation, the image subject may move within theimage frame (i.e., due to camera movement or subject movement). Anexaggerated view of this effect is shown in FIG. 40, depicting an imaged“E” captured as the sensor is moved to the left. The vertical stroke ofthe letter is further from the left edge of the image frame at thebottom than the top, due to movement of the sensor while the pixel datais being clocked-out.

The phenomenon also arises when the camera assembles data from severalframes to generate a single “still” image. Often unknown to the user,many consumer imaging devices rapidly capture plural frames of imagedata, and composite different aspects of the data together (usingsoftware provided, e.g., by FotoNation, Inc., now Tessera Technologies,Inc.). For example, the device may take three exposures—one exposed tooptimize appearance of faces detected in the image frame, anotherexposed in accordance with the background, and other exposed inaccordance with the foreground. These are melded together to create apleasing montage. (In another example, the camera captures a burst offrames and, in each, determines whether persons are smiling or blinking.It may then select different faces from different frames to yield afinal image.)

Thus, the distinction between video and still imagery is no longersimply a device modality, but rather is becoming a user modality.

Detection of motion can be accomplished in the spatial domain (e.g., byreference to movement of feature pixels between frames), or in atransform domain. Fourier transform and DCT data are exemplary. Thesystem may extract the transform domain signature of an image component,and track its movement across different frames—identifying its motion.One illustrative technique deletes, e.g., the lowest N frequencycoefficients—leaving just high frequency edges, etc. (The highest Mfrequency coefficients may be disregarded as well.) A thresholdingoperation is performed on the magnitudes of the remainingcoefficients—zeroing those below a value (such as 30% of the mean). Theresulting coefficients serve as the signature for that image region.(The transform may be based, e.g., on tiles of 8×8 pixels.) When apattern corresponding to this signature is found at a nearby locationwithin another (or the same) image frame (using known similaritytesting, such as correlation), movement of that image region can beidentified.

In many systems it is desirable to perform a set of processing steps(like those detailed above) that extract information about the incomingcontent (e.g., image data) in a scalable (e.g., distributed) manner.This extracted information (metadata) is then desirably packaged tofacilitate subsequent processing (which may be application specific, ormore computationally intense, and can be performed within theoriginating device or by a remote system).

A rough analogy is user interaction with Google. Bare search termsaren't sent to a Google mainframe, as if from a dumb terminal. Instead,the user's computer formats a query as an HTTP request, including theinternet protocol address of the originating computer (indicative oflocation), and makes available cookie information by which user languagepreferences, desired safe search filtering, etc., can be discerned. Thisstructuring of relevant information serves as a precursor to Google'ssearch process, allowing Google to perform the search process moreintelligently—providing faster and better results to the user.

FIG. 41 shows some of the metadata that may be involved in an exemplarysystem. The left-most column of information types may be computeddirectly from the native image data signals taken from the image sensor.(As noted, some or all of these can be computed using processingarrangements integrated with the sensor on a common substrate.)Additional information may be derived by reference to these basic datatypes, as shown by the second column of information types. This furtherinformation may be produced by processing in the cell phone, or externalservices can be employed (e.g., the OCR recognition service shown inFIG. 37 can be within the cell phone, or can be a remote server, etc.;similarly with the operations shown in FIG. 30.).

How can this information be packaged to facilitate subsequentprocessing? One alternative is to convey it in the “alpha” channel ofcommon image formats.

Most image formats represent imagery by data conveyed in pluralchannels, or byte-planes. In RGB, for example, one channel conveys redluminance, a second conveys green luminance, and a third conveys blueluminance. Similarly with CMYK (the channels respectively conveyingcyan, magenta, yellow, and black information) Ditto with YUV—commonlyused with video (a luma, or brightness, channel: Y, and two colorchannels: U and V), and LAB (also brightness, with two color channels).

These imaging constructs are commonly extended to include an additionalchannel: alpha. The alpha channel is provided to convey opacityinformation—indicating the extent to which background subjects arevisible through the imagery.

While commonly supported by image processing file structures, softwareand systems, the alpha channel is not much used (except, most notably,in computer generated imagery and radiology). Certain implementations ofthe present technology use the alpha channel to transmit informationderived from image data.

The different channels of image formats commonly have the same size andbit-depth. In RGB, for example, the red channel may convey 8-bit data(allowing values of 0-255 to be represented), for each pixel in a640×480 array. Likewise with the green and blue channels. The alphachannel in such arrangements is also commonly 8 bits, and co-extensivewith the image size (e.g., 8 bits×640×480). Every pixel thus has a redvalue, a green value, a blue value, and an alpha value. (The compositeimage representation is commonly known as RGBA.)

A few of the many ways the alpha channel can be used to conveyinformation derived from the image data are shown in FIGS. 42-51, anddiscussed below.

FIG. 42 shows a picture that a user may snap with a cell phone. Aprocessor in the cell phone (on the sensor substrate or elsewhere) mayapply an edge detection filter (e.g., a Sobel filter) to the image data,yielding an edge map. Each pixel of the image is either determined to bepart of an edge, or not. So this edge information can be conveyed injust one bit plane of the eight bit planes available in the alphachannel. Such an alpha channel payload is shown in FIG. 43.

The cell phone camera may also apply known techniques to identify faceswithin the image frame. The red, green and blue image data from pixelscorresponding to facial regions can be combined to yield a grey-scalerepresentation, and this representation can be included in the alphachannel—e.g., in aligned correspondence with the identified faces in theRGB image data. An alpha channel conveying both edge information andgreyscale faces is shown in FIG. 44. (An 8-bit greyscale is used forfaces in the illustrated embodiment, although a shallower bit-depth,such as 6- or 7-bits, can be used in other arrangements—freeing otherbit planes for other information.)

The camera may also perform operations to locate the positions of theeyes and mouth in each detected face. Markers can be transmitted in thealpha channel—indicating the scale and positions of these detectedfeatures. A simple form of marker is a “smiley face” bit-mapped icon,with the eyes and mouth of the icon located at the positions of thedetected eyes and mouth. The scale of the face can be indicated by thelength of the iconic mouth, or by the size of a surrounding oval (or thespace between the eye markers). The tilt of the face can be indicated bythe angle of the mouth (or the angle of the line between the eyes, orthe tilt of a surrounding oval).

If the cell phone processing yields a determination of the genders ofpersons depicted in the image, this too can be represented in the extraimage channel. For example, an oval line circumscribing the detectedface of a female may be made dashed or otherwise patterned. The eyes maybe represented as cross-hairs or Xs instead of blackened circles, etc.Ages of depicted persons may also be approximated, and indicatedsimilarly. The processing may also classify each person's emotionalstate by visual facial clues, and an indication such assurprise/happiness/sadness/anger/neutral can be represented. (See, e.g.,Su, “A simple approach to facial expression recognition,” Proceedings ofthe 2007 Int'l Conf on Computer Engineering and Applications,Queensland, Australia, 2007, pp. 456-461.)

When a determination has some uncertainty (such as guessing gender, agerange, or emotion), a confidence metric output by the analysis processcan also be represented in an iconic fashion, such as by the width ofthe line, or the scale or selection of pattern elements.

FIG. 45 shows different pattern elements that can be used to denotedifferent information, including gender and confidence, in an auxiliaryimage plane.

The portable device may also perform operations culminating in opticalcharacter recognition of alphanumeric symbols and strings depicted inthe image data. In the illustrated example, the device may recognize thestring “LAS VEGAS” in the picture. This determination can bememorialized by a PDF417 2D barcode added to the alpha channel. Thebarcode can be in the position of the OCR'd text in the image frame, orelsewhere.

(PDF417 is exemplary only. Other barcodes—such as 1D, Aztec, Datamatrix,High Capacity Color Barcode, Maxicode, QR Code, Semacode, andShotCode—or other machine-readable data symbologies—such as OCR fontsand data glyphs—can naturally be used. Glyphs can be used both to conveyarbitrary data, and also to form halftone image depictions. See in thisregard U.S. Pat. No. 6,419,162, and Hecht, “Printed Embedded DataGraphical User Interfaces,” IEEE Computer Magazine, Vol. 34, No. 3,2001, pp 47-55.)

FIG. 46 shows an alpha channel representation of some of the informationdetermined by the device. All of this information is structured in amanner that allows it to be conveyed within just a single bit plane (ofthe eight bit planes) of the alpha channel. Information resulting fromother of the processing operations (e.g., the analyses shown in FIGS. 30and 41) may be conveyed in this same bit plane, or in others.

While FIGS. 42-46 showed a variety of information that can be conveyedin the alpha channel, and different representations of same, still moreare shown in the example of FIGS. 47-49. These involve a cell phonepicture of a new GMC truck and its owner.

Among other processing, the cell phone in this example processed theimage data to recognize the model, year and color of the truck,recognize the text on the truck grill and the owner's t-shirt, recognizethe owner's face, and recognize areas of grass and sky.

The sky was recognized by its position at the top of the frame, itscolor histogram within a threshold distance of expected norms, and aspectral composition weak in high frequency coefficients. The grass wasrecognized by its texture and color. (Other techniques for recognizingthese features are taught, e.g., in Batlle, “A review on strategies forrecognizing natural objects in colour images of outdoor scenes,” Imageand Vision Computing, Volume 18, Issues 6-7, 1 May 2000, pp. 515-530;Hayashi, “Fast Labelling of Natural Scenes Using Enhanced Knowledge,”Pattern Analysis & Applications, Volume 4, Number 1/March, 2001, pp.20-27; and Boutell, “Improved semantic region labeling based on scenecontext,” IEEE Int'l Conf. on Multimedia and Expo, July, 2005.) Thetrees could have been similarly recognized.

The human face in the image was detected using arrangements like thosecommonly employed in consumer cameras. Optical character recognition wasperformed on a data set resulting from application of an edge detectionalgorithm to the input image, followed by Fourier and Mellin transforms.(While finding the text GMC and LSU TIGERS, the algorithm failed toidentify other text on the t-shirt, and text on the tires. Withadditional processing time, some of this missing text may have beendecoded.)

The truck was first classed as a vehicle, and then as a truck, and thenfinally identified as a Dark Crimson Metallic 2007 GMC Sierra Z-71 withextended cab, by pattern matching. (This detailed identification wasobtained through use of known reference truck images, from resourcessuch as the GM trucks web site, Flickr, and a fan site devoted toidentifying vehicles in Hollywood motion pictures: IMCDB<dot>com.)

FIG. 48 shows an illustrative graphical, bitonal representation of thediscerned information, as added to the alpha channel of the FIG. 47image. (FIG. 49 shows the different planes of the composite image: red,green, blue, and alpha.)

The portion of the image area detected as depicting grass is indicatedby a uniform array of dots. The image area depicting sky is representedas a grid of lines. (If trees had been particularly identified, theycould be labeled using one of the same patterns, but with differentsize/spacing/etc. Or an entirely different pattern could be used.)

The identification of the truck as a Dark Crimson Metallic 2007 GMCSierra Z-71 with extended cab is encoded in a PDF417 2D barcode—scaledto the size of the truck and masked by its shape. Because PDF417 encodesinformation redundantly, with error-correction features, the portions ofthe rectangular barcode that are missing do not prevent the encodedinformation from being recovered.

The face information is encoded in a second PDF417 barcode. This secondbarcode is oriented at 90 degrees relative to the truck barcode, and isscaled differently, to help distinguish the two distinct symbols todownstream decoders. (Other different orientations could be used, and insome cases are preferable, e.g., 30 degrees, 45 degrees, etc.)

The facial barcode is oval in shape, and may be outlined with an ovalborder (although this is not depicted). The center of the barcode isplaced at the mid-point of the person's eyes. The width of the barcodeis twice the distance between the eyes. The height of the oval barcodeis four times the distance between the mouth and a line joining theeyes.

The payload of the facial barcode conveys information discerned from theface. In rudimentary embodiments, the barcode simply indicates theapparent presence of a face. In more sophisticated embodiments,eigenvectors computed from the facial image can be encoded. If aparticular face is recognized, information identifying the person can beencoded. If the processor makes a judgment about the likely gender ofthe subject, this information can be conveyed in the barcode too.

Persons appearing in imagery captured by consumer cameras and cellphones are not random: a significant percentage are of recurringsubjects, e.g., the owner's children, spouse, friends, the userhimself/herself, etc. There are often multiple previous images of theserecurring subjects distributed among devices owned or used by the owner,e.g., PDA, cell phone, home computer, network storage, etc. Many ofthese images are annotated with names of the persons depicted. From suchreference images, sets of characterizing facial vectors can be computed,and used to identify subjects in new photos. (As noted, Google's Picasaservice works on this principle to identify persons in a user's photocollection.) Such a library of reference facial vectors can be checkedto try and identify the person depicted in the FIG. 47 photograph, andthe identification can be represented in the barcode. (Theidentification can comprise the person's name, and/or otheridentifier(s) by which the matched face is known, e.g., an index numberin a database or contact list, a telephone number, a FaceBook user name,etc.)

Text recognized from regions of the FIG. 47 image is added tocorresponding regions of the alpha channel frame, presented in areliably decodable OCR font. (OCR-A is depicted although other fonts maybe used.)

A variety of further information could be included in the FIG. 48 alphachannel. For example, locations in the frame where a processor suspectstext is present, but OCRing did not successfully decode alphanumericsymbols (on the tires perhaps, or other characters on the person'sshirt), can be identified by adding a corresponding visual clue (e.g., apattern of diagonal lines). An outline of the person (rather than justan indication of his face) can also be detected by a processor, andindicated by a corresponding border or fill pattern.

While the examples of FIGS. 42-46 and FIGS. 47-49 show various differentways of representing semantic metadata in the alpha channel, still moretechniques are shown in the example of FIGS. 50-51. Here a user hascaptured a snapshot of a child at play (FIG. 50).

The child's face is turned away from the camera, and is captured withpoor contrast. However, even with this limited information, theprocessor makes a likely identification by referring to the user'sprevious images: the user's firstborn child Matthew Doe (who seems to befound in countless of the user's archived photos).

As shown in FIG. 51, the alpha channel in this example conveys anedge-detected version of the user's image. Superimposed over the child'shead is a substitute image of the child's face. This substitute imagecan be selected for its composition (e.g., depicting two eyes, nose andmouth) and better contrast.

In some embodiments, each person known to the system has an iconicfacial image that serves as a visual proxy for the person in differentcontexts. For example, some PDAs store contact lists that include facialimages of the contacts. The user (or the contacts) provides facialimages that are easily recognized—iconic. These iconic facial images canbe scaled to match the head of the person depicted in an image, andadded to the alpha channel at the corresponding facial location.

Also included in the alpha channel depicted in FIG. 51 is a 2D barcode.This barcode can convey other of the information discerned fromprocessing of the image data or otherwise available (e.g., the child'sname, a color histogram, exposure metadata, how many faces were detectedin the picture, the ten largest DCT coefficients, etc.).

To make the 2D barcode as robust as possible to compression and otherimage processing operations, its size may not be fixed, but rather isdynamically scaled based on circumstances—such as image characteristics.In the depicted embodiment, the processor analyzes the edge map toidentify regions with uniform edginess (i.e., within a thresholdedrange). The largest such region is selected. The barcode is then scaledand placed to occupy a central area of this region. (In subsequentprocessing, the edginess where the barcode was substituted can belargely recovered by averaging the edginess at the center pointsadjoining the four barcode sides.)

In another embodiment, region size is tempered with edginess indetermining where to place a barcode: low edginess is preferred. In thisalternative embodiment, a smaller region of lower edginess may be chosenover a larger region of higher edginess. The size of each candidateregion, minus a scaled value of edginess in the region, can serve as ametric to determine which region should host the barcode. This is thearrangement used in FIG. 51, resulting in placement of the barcode in aregion to the left of Matthew's head—rather than in a larger, butedgier, region to the right.

Although the FIG. 50 photo is relatively “edgy” (as contrasted, e.g.,with the FIG. 42 photo), much of the edginess may be irrelevant. In someembodiments the edge data is filtered to preserve only the principaledges (e.g., those indicated by continuous line contours). Withinotherwise vacant regions of the resulting filtered edge map a processorcan convey additional data. In one arrangement the processor inserts apattern to indicate a particular color histogram bin into which thatregion's image colors fall. (In a 64-bin histogram, requiring 64different patterns, bin 2 may encompass colors in which the red channelhas values of 0-63, the green channel has values of 0-63, and the bluechannel has a values of 64-127, etc.) Other image metrics can similarlybe conveyed.

Instead of using different patterns to indicate different data, vacantregions in a filtered edge map can be filled with a noise-likesignal—steganographically encoded to convey histogram (or otherinformation) as digital watermark data. (A suitable watermarkingtechnology is detailed in U.S. Pat. No. 6,590,996.)

It will be recognized that some of the information in the alphachannel—if visually presented to a human in a graphical form, conveysuseful information. From FIG. 43 a human can distinguish a man embracinga woman, in front of a sign stating “WELCOME TO Fabulous LAS VEGASNEVADA.” From FIG. 44 the human can see greyscale faces, and an outlineof the scene. From FIG. 46 the person can additionally identify abarcode conveying some information, and can identify two smiley faceicons showing the positions of faces.

Likewise, a viewer to whom the frame of graphical information in FIG. 48is rendered can identify an outline of a person, can read the LSU TIGERSfrom the person's shirt, and make out what appears to be the outline ofa truck (aided by the clue of the GMC text where the truck's grill wouldbe).

From presentation of the FIG. 51 alpha channel data a human can identifya child sitting on the floor, playing with toys.

The barcode in FIG. 51, like the barcode in FIG. 46, conspicuouslyindicates to an inspecting human the presence of information, albeit notits content.

Other of the graphical content in the alpha channel may not beinformative to a human upon inspection. For example, if the child's nameis steganographically encoded as a digital watermark in a noise-likesignal in FIG. 51, even the presence of information in that noise may goundetected by the person.

The foregoing examples detail some of the diversity of semanticinformation that can be stuffed into the alpha channel, and thediversity of representation constructs that can be employed. Of course,this is just a small sampling; the artisan can quickly adapt theseteachings to the needs of particular applications, yielding many other,different embodiments. Thus, for example, any of the information thatcan be extracted from an image can be memorialized in the alpha channelusing arrangements akin to those disclosed herein.

It will be recognized that information relating to the image can beadded to the alpha channel at different times, by different processors,at different locations. For example, the sensor chip in a portabledevice may have on-chip processing that performs certain analyses, andadds resulting data to the alpha channel. The device may have anotherprocessor that performs further processing—on the image data and/or onthe results of the earlier analyses—and adds a representation of thosefurther results to the alpha channel. (These further results may bebased, in part, on data acquired wirelessly from a remote source. Forexample, a consumer camera may link by Bluetooth to the user's PDA, toobtain facial information from the user's contact files.)

The composite image file may be transmitted from the portable device toan intermediate network node (e.g., at Verizon, AT&T or T-Mobile), whichperforms additional processing, and adds its results to the alphachannel. (With its more capable processing hardware, such anintermediate network node can perform more complex, resource-intensiveprocessing—such as more sophisticated facial recognition and patternmatching. With its higher-bandwidth network access, such a node can alsoemploy a variety of remote resources to augment the alpha channel withadditional data, e.g., links to Wikipedia entries—or Wikipedia contentitself, information from telephone database and image database lookups,etc.) The thus-supplemented image may then be forwarded to an imagequery service provider (e.g., SnapNow, MobileAcuity, etc.), which cancontinue the process and/or instruct a responsive action based on theinformation thus-provided.

In the described arrangement the alpha channel thus conveys an iconicview of what all preceding processing has discerned or learned about theimage. Each subsequent processor can readily access this information,and contribute still more. All this within the existing workflowchannels and constraints of long-established file formats.

In some embodiments, the provenance of some or all of thediscerned/inferred data is indicated. For example, stored data mayindicate that OCRing which yielded certain text was performed by aVerizon server having a unique identifier, such as MAC address of01-50-F3-83-AB-CC or network identifierPDX-LA002290.corp.verizon-dot-com, on Aug. 28, 2008, 8:35 pm. Suchinformation can be stored in the alpha channel, in header data, in aremote repository to which a pointer is provided, etc.

Different processors may contribute to different bit-planes of the alphachannel. A capture device may write its information to bit plane #1. Anintermediate node may store its contributions in bit plane #2. Etc.Certain bit planes may be available for shared use.

Or different bit planes may be allocated for different classes or typesof semantic information. Information relating to faces or persons in theimage may always be written to bit plane #1. Information relating toplaces may always be written to bit plane #2. Edge map data may alwaysbe found in bit plane #3, together with color histogram data (e.g.,represented in 2D barcode form). Other content labeling (e.g., grass,sand, sky) may be found in bit plane #4, together with OCR'd text.Textual information, such as related links or textual content obtainedfrom the web may be found in bit plane #5. (ASCII symbols may beincluded as bit patterns, e.g., with each symbol taking 8 bits in theplane. Robustness to subsequent processing can be enhanced by allocating2 or more bits in the image plane for each bit of ASCII data.Convolutional coding and other error correcting technologies can also beemployed. So, too, can error correcting barcodes.)

An index to the information conveyed in the alpha channel can becompiled, e.g., in an EXIF header associated with the image, allowingsubsequent systems to speed their interpretation and processing of suchdata. The index can employ XML-like tags, specifying the types of dataconveyed in the alpha channel, and optionally other information (e.g.,their locations).

Locations can be specified as the location of the upper-most bit (orupper-left-most bit) in the bit-plane array, e.g., by X-, Y-coordinates.Or a rectangular bounding box can be specified by reference to a pairX-, Y-coordinates—detailing the region where information is represented.

In the example of FIG. 46, the index may convey information such as

<MaleFace1> AlphaBitPlane1 (637,938) </MaleFace1> <FemaleFace1>AlphaBitPlane1 (750,1012) </FemaleFace1> <OCRTextPDF417> AlphaBitPlane1(75,450)-(1425,980) </OCRTextPDF417> <EdgeMap> AlphaBitPlane1 </EdgeMap>

This index thus indicates that a male face is found in bit plane #1 ofthe alpha channel, with a top pixel at location (637,938); a female faceis similarly present with a top pixel located at (750,1012); OCR'd textencoded as a PDF417 barcode is found in bit plane #1 in the rectangulararea with corner points (75,450) and (1425,980), and that bit plane #1also includes an edge map of the image.

More or less information can naturally be provided. A different form ofindex, with less information, may specify, e.g.:

<AlphaBitPlane1> Face,Face,PDF417,EdgeMap </AlphaBitPlane1>

This form of index simply indicates that bit plane #1 of the alphachannel includes 2 faces, a PDF417 barcode, and an edge map.

An index with more information may specify data including the rotationangle and scale factor for each face, the LAS VEGAS payload of thePDF417 barcode, the angle of the PDF417 barcode, the confidence factorsfor subjective determinations, names of recognized persons, a lexicon orglossary detailing the semantic significance of each pattern used in thealpha channels (e.g., the patterns of FIG. 45, and the graphical labelsused for sky and grass in FIG. 48), the sources of auxiliary data (e.g.,of the superimposed child's face in FIG. 51, or the remote referenceimage data that served as basis for the conclusion that the truck inFIG. 47 is a Sierra Z71), etc.

As can be seen, the index can convey information that is also conveyedin the bit planes of the alpha channel. Generally different forms ofrepresentation are used in the alpha channel's graphicalrepresentations, versus the index. For example, in the alpha channel thefemaleness of the second face is represented by the ‘+’s to representthe eyes; in the index the femaleness is represented by the XML tag<FemaleFace1>. Redundant representation of information can serve as acheck on data integrity.

Sometimes header information, such as EXIF data, becomes separated fromthe image data (e.g., when the image is converted to a differentformat). Instead of conveying index information in a header, a bit planeof the alpha channel can serve to convey the index information, e.g.,bit plane #1. One such arrangement encodes the index information as a 2Dbarcode. The barcode may be scaled to fill the frame, to provide maximumrobustness to possible image degradation.

In some embodiments, some or all of the index information is replicatedin different data stores. For example, it may be conveyed both in EXIFheader form, and as a barcode in bit plane #1. Some or all of the datamay also be maintained remotely, such as by Google, or other web storage“in the cloud.” Address information conveyed by the image can serve as apointer to this remote storage. The pointer (which can be a URL, butmore commonly is a UID or index into a database which—whenqueried—returns the current address of the sought-for data) can beincluded within the index, and/or in one or more of the bit planes ofthe alpha channel. Or the pointer can be steganographically encodedwithin the pixels of the image data (in some or all of the compositeimage planes) using digital watermarking technology.

In still other embodiments, some or all the information described aboveas stored in the alpha channel can additionally, or alternatively, bestored remotely, or encoded within the image pixels as a digitalwatermark. (The picture itself, with or without the alpha channel, canalso be replicated in remote storage, by any device in the processingchain.)

Some image formats include more than the four planes detailed above.Geospatial imagery and other mapping technologies commonly representdata with formats that extend to a half-dozen or more informationplanes. For example, multispectral space-based imagery may have separateimage planes devoted to (1) red, (2) green, (3) blue, (4) near infrared,(5) mid-infrared, (6) far infrared, and (7) thermal infrared. Thetechniques detailed above can convey derived/inferred image informationusing one or more of the auxiliary data planes available in suchformats.

As an image moves between processing nodes, some of the nodes mayoverwrite data inserted by earlier processing. Although not essential,the overwriting processor may copy the overwritten information intoremote storage, and include a link or other reference to it in the alphachannel, or index, or image—in case same later is needed.

When representing information in the alpha channel, consideration may begiven to degradations to which this channel may be subjected. JPEGcompression, for example, commonly discards high frequency details thatdo not meaningfully contribute to a human's perception of an image. Suchdiscarding of information based on the human visual system, however, canwork to disadvantage when applied to information that is present forother purposes (although human viewing of the alpha channel is certainlypossible and, in some cases, useful).

To combat such degradation, the information in the alpha channel isdesirably represented by features that would not likely be regarded asvisually irrelevant. Different types of information may be representedby different features, so that the most important persist through evensevere compression. Thus, for example, the presence of faces in FIG. 46are signified by bold ovals. The locations of the eyes may be lessrelevant, so are represented by smaller features. Patterns shown in FIG.45 may not be reliably distinguished after compression, and so might bereserved to represent secondary information—where loss is lessimportant. With JPEG compression, the most-significant bit-plane is bestpreserved, whereas lesser-significant bit-planes are increasinglycorrupted. Thus, the most important metadata should be conveyed in themost-significant bit planes of the alpha channel—to enhancesurvivability.

If technology of the sort illustrated by FIGS. 42-51 becomes a linguafranca for conveying metadata, image compression might evolve to takeits presence into account. For example, JPEG compression may be appliedto the red, green and blue image channels, but lossless (or less lossy)compression may be applied to the alpha channel. Since the various bitplanes of the alpha channel may convey different information, they maybe compressed separately—rather than as bytes of 8-bit depth. (Ifcompressed separately, lossy compression may be more acceptable.) Witheach bit-plane conveying only bitonal information, compression schemesknown from facsimile technology can be used, including Modified Huffman,Modified READ, run length encoding, and ITU-T T.6. Hybrid compressiontechniques are thus well-suited for such files.

Alpha channel conveyance of metadata can be arranged to progressivelytransmit and decode in general correspondence with associated imageryfeatures, when using compression arrangements such as JPEG 2000. Thatis, since the alpha channel is presenting semantic information in thevisual domain (e.g., iconically), it can be represented so that layersof semantic detail decompress at the same rate as the image.

In JPEG 2000, a wavelet transform is used to generate data representingthe image. JPEG 2000 packages and processes this transform data in amanner yielding progressive transmission and decoding. For example, whenrendering a JPEG 2000 image, the gross details of the image appearfirst, with successively finer details following. Similarly withtransmission.

Consider the truck & man image of FIG. 47. Rendering a JPEG 2000 versionof this image would first present the low frequency, bold form of thetruck. Thereafter the shape of the man would appear. Next, features suchas the GMC lettering on the truck grill, and the logo on the man'st-shirt would be distinguished. Finally, the detail of the man's facialfeatures, the grass, the trees, and other high frequency minutiae wouldcomplete the rendering of the image. Similarly with transmission.

This progression is shown in the pyramid of FIG. 56A. Initially arelatively small amount of information is presented—giving gross shapedetails. Progressively the image fills in—ultimately ending with arelatively large amount of small detail data.

The information in the alpha channel can be arranged similarly.Information about the truck can be represented with a large, lowfrequency (shape-dominated) symbology. Information indicating thepresence and location of the man can be encoded with anext-most-dominant representation. Information corresponding to the GMClettering on the truck grill, and lettering on the man's shirt, can berepresented in the alpha channel with a finer degree of detail. Thefinest level of salient detail in the image, e.g., the minutiae of theman's face, can be represented with the finest degree of detail in thealpha channel. (As may be noted, the illustrative alpha channel of FIG.48 doesn't quite follow this model.)

If the alpha channel conveys its information in the form ofmachine-readable symbologies (e.g., barcodes, digital watermarks,glyphs, etc.), the order of alpha channel decoding can bedeterministically controlled. Features with the largest features aredecoded first; those with the finest features are decoded last. Thus,the alpha channel can convey barcodes at several different sizes (all inthe same bit frame, e.g., located side-by-side, or distributed among bitframes). Or the alpha channel can convey plural digital watermarksignals, e.g., one at a gross resolution (e.g., corresponding to 10watermark elements, or “waxels” to the inch), and others at successivelyfiner resolutions (e.g., 50, 100, 150 and 300 waxels per inch). Likewisewith data glyphs: a range of larger and smaller sizes of glyphs can beused, and they will decode relatively earlier or later.

(JPEG 2000 is the most common of the compression schemes exhibitingprogressive behavior, but there are others. JPEG, with some effort, canbehave similarly. The present concepts are applicable whenever suchprogressivity exists.)

By such arrangements, as image features are decoded for presentation—ortransmitted (e.g., by streaming media delivery), the correspondingmetadata becomes available.

It will be recognized that results contributed to the alpha channel bythe various distributed processing nodes are immediately available toeach subsequent recipient of the image. A service provider receiving aprocessed image, for example, thus quickly understands that FIG. 42depicts a man and a woman in Las Vegas; that FIG. 43 shows a man and hisGMC truck; and that the FIG. 50 image shows a child named Matthew Doe.Edge map, color histogram, and other information conveyed with theseimages gives the service provider a headstart in its processing of theimagery, e.g., to segment it; recognize its content, etc.

Receiving nodes can also use the conveyed data to enhance stored profileinformation relating to the user. A node receiving the FIG. 46 metadatacan note Las Vegas as a location of potential interest. A systemreceiving the FIG. 48 metadata can infer that GMC Z71 trucks arerelevant to the user, and/or to the person depicted in that photo. Suchassociations can serve as launch points for tailored user experiences.

The metadata also allows images with certain attributes to be identifiedquickly, in response to user queries. (E.g., find pictures showing GMCSierra Z71 trucks.) Desirably, web-indexing crawlers can check the alphachannels of images they find on the web, and add information from thealpha channel to the compiled index to make the image more readilyidentifiable to searchers.

As noted, an alpha channel-based approach is not essential for use ofthe technologies detailed in this specification. Another alternative isa data structure indexed by coordinates of image pixels. The datastructure can be conveyed with the image file (e.g., as EXIF headerdata), or stored at a remote server.

For example, one entry in the data structure corresponding to pixel(637,938) in FIG. 46 may indicate that the pixel forms part of a male'sface. A second entry for this pixel may point to a shared sub-datastructure at which eigenface values for this face are stored. (Theshared sub-data structure may also list all the pixels associated withthat face.) A data record corresponding to pixel (622,970) may indicatethe pixel corresponds to the left-side eye of the male's face. A datarecord indexed by pixel (155,780) may indicate that the pixel forms partof text recognized (by OCRing) as the letter “L”, and also falls withincolor histogram bin 49, etc. The provenance of each datum of informationmay also be recorded.

(Instead of identifying each pixel by X- and Y-coordinates, each pixelmay be assigned a sequential number by which it is referenced.)

Instead of several pointers pointing to a common sub-data structure fromdata records of different pixels, the entries may form a linked list, inwhich each pixel includes a pointer to a next pixel with a commonattribute (e.g., associated with the same face). A record for a pixelmay include pointers to plural different sub-data structures, or toplural other pixels—to associate the pixel with plural different imagefeatures or data.

If the data structure is stored remotely, a pointer to the remote storecan be included with the image file, e.g., steganographically encoded inthe image data, expressed with EXIF data, etc. If any watermarkingarrangement is used, the origin of the watermark (see U.S. Pat. No.6,307,949) can be used as a base from which pixel references arespecified as offsets (instead of using, e.g., the upper left corner ofthe image). Such an arrangement allows pixels to be correctly identifieddespite corruptions such as cropping, or rotation.

As with alpha channel data, the metadata written to a remote store isdesirably available for search. A web crawler encountering the image canuse the pointer in the EXIF data or the steganographically encodedwatermark to identify a corresponding repository of metadata, and addmetadata from that repository to its index terms for the image (despitebeing found at different locations).

By the foregoing arrangements it will be appreciated that existingimagery standards, workflows, and ecosystems—originally designed tosupport just pixel image data, are here employed in support of metadataas well.

(Of course, the alpha channel and other approaches detailed in thissection are not essential to other aspects of the present technology.For example, information derived or inferred from processes such asthose shown in FIGS. 30, 37 and 41 can be sent by other transmissionarrangements, e.g., dispatched as packetized data using WiFi or WiMax,transmitted from the device using Bluetooth, sent as an SMS short textor MMS multimedia messages, shared to another node in a low powerpeer-to-peer wireless network, conveyed with wireless cellulartransmission or wireless data service, etc.)

Texting, Etc.

U.S. Pat. Nos. 5,602,566, 6,115,028, 6,201,554, 6,466,198, 6,573,883,6,624,824 and 6,956,564, and published PCT application WO9814863, teachthat portable computers can be equipped with devices by which tiltingcan be sensed, and used for different purposes (e.g., scrolling throughmenus).

In accordance with another aspect of the present technology, a tip/tiltinterface is used in connection with a typing operation, such as forcomposing text messages sent by a Simple Message Service (SMS) protocolfrom a PDA, a cell phone, or other portable wireless device.

In one embodiment, a user activates a tip/tilt text entry mode using anyof various known means (e.g., pushing a button, entering a gesture,etc.). A scrollable user interface appears on the device screen,presenting a series of icons. Each icon has the appearance of a cellphone key, such as a button depicting the numeral “2” and the letters“abc.” The user tilts the device left or right to scroll backwards orforwards thru the series of icons, to reach a desired button. The userthen tips the device towards or away from themselves to navigate betweenthe three letters associated with that icon (e.g., tipping awaynavigates to “a;” no tipping corresponds to “b;” and tipping towardsnavigates to “c”). After navigating to the desired letter, the usertakes an action to select that letter. This action may be pressing abutton on the device (e.g., with the user's thumb), or another actionmay signal the selection. The user then proceeds as described to selectsubsequent letters. By this arrangement, the user enters a series oftext without the constraints of big fingers on tiny buttons or UIfeatures.

Many variations are, of course, possible. The device may have atouch-screen. After navigating to a desired character, the user may tapthe touch screen to effect the selection. When tipping/tilting thedevice, the corresponding letter can be displayed on the screen in anenlarged fashion (e.g., on the icon representing the button, or overlaidelsewhere) to indicate the user's progress in navigation.

While accelerometers or other physical sensors are employed in certainembodiments, others use a 2D optical sensor (e.g., a camera). The usercan point the sensor to the floor, to a knee, or to another subject, andthe device can then sense relative physical motion by sensing movementof features within the image frame (up/down; left right). In suchembodiments the image frame captured by the camera need not be presentedon the screen; the symbol selection UI, alone, may be displayed. (Or,the UI can be presented as an overlay on the background image capturedby the camera.)

In camera-based embodiments, as with embodiments employing physicalsensors, another dimension of motion may also be sensed: up/down. Thiscan provide an additional degree of control (e.g., shifting to capitalletters, or shifting from characters to numbers, or selecting thecurrent symbol, etc).

In some embodiments, the device has several modes: one for enteringtext; another for entering numbers; another for symbols; etc. The usercan switch between these modes by using mechanical controls (e.g.,buttons), or through controls of a user interface (e.g., touches orgestures or voice commands). For example, while tapping a first regionof the screen may select the currently-displayed symbol, tapping asecond region of the screen may toggle the mode between character-entryand numeric-entry. Or one tap in this second region can switch tocharacter-entry (the default); two taps in this region can switch tonumeric-entry; and three taps in this region can switch to entry ofother symbols.

Instead of selecting between individual symbols, such an interface canalso include common words or phrases (e.g., signature blocks) to whichthe user can tip/tilt navigate, and then select. There may be severallists of words/phrases. For example, a first list may be standardized(pre-programmed by the device vendor), and include statistically commonwords. A second list may comprise words and/or phrases that areassociated with a particular user (or a particular class of users). Theuser may enter these words into such a list, or the device can compilethe list during operation—determining which words are most commonlyentered by the user. (The second list may exclude words found on thefirst list, or not.) Again, the user can switch between these lists asdescribed above.

Desirably, the sensitivity of the tip/tilt interface is adjustable bythe user, to accommodate different user preferences and skills

While the foregoing embodiments contemplated a limited grammar oftilts/tips, more expansive grammars can be devised. For example, whilerelative slow tilting of the screen to the left may cause the icons toscroll in a given direction (left, or right, depending on theimplementation), a sudden tilt of the screen in that direction caneffect a different operation—such as inserting a line (or paragraph)break in the text. A sharp tilt in the other direction can cause thedevice to send the message.

Instead of the speed of tilt, the degree of tilt can correspond todifferent actions. For example, tilting the device between 5 and 25degrees can cause the icons to scroll, but tilting the device beyond 30degrees can insert a line break (if to the left) or can cause themessage to be sent (if to the right).

Different tip gestures can likewise trigger different actions.

The arrangements just described are necessarily only a few of the manydifferent possibilities. Artisans adopting such technology are expectedto modify and adapt these teachings as suited for particularapplications.

Affine Capture Parameters

In accordance with another aspect of the present technology, a portabledevice captures—and may present—geometric information relating to thedevice's position (or that of a subject).

U.S. Pat. No. 8,565,815 teaches various arrangements by which a cellphone can be made responsive to what it “sees,” including overlayinggraphical features atop certain imaged objects. The overlay can bewarped in accordance with the object's perceived affine distortion.

Steganographic calibration signals by which affine distortion of animaged object can be accurately quantified are detailed, e.g., in U.S.Pat. Nos. 6,614,914 and 6,580,809; and in patent publicationsUS20040105569, US20040101157, and US20060031684. U.S. Pat. No. 6,959,098teaches how distortion can be characterized by such watermarkcalibration signals in conjunction with visible image features (e.g.,edges of a rectilinear object). From such affine distortion information,the 6D location of a watermarked object (x, y, z, tip, tilt, rotation)relative to the imager of a cell phone can be determined.

FIG. 38 shows how a cell phone can display affine parameters (e.g.,derived from imagery or otherwise). The camera can be placed in thismode through a UI control (e.g., tapping a physical button, making atouchscreen gesture, etc.)

In the depicted arrangement, the device's rotation from (an apparent)horizontal orientation is presented at the top of the cell phone screen.The cell phone processor can make this determination by analyzing theimage data for one or more generally parallel elongated straight edgefeatures, averaging them to determine a mean, and assuming that this isthe horizon. If the camera is conventionally aligned with the horizon,this mean line will be horizontal. Divergence of this line fromhorizontal indicates the camera's rotation. This information can bepresented textually (e.g., “12 degrees right”), and/or a graphicalrepresentation showing divergence from horizontal can be presented.

In the illustrated embodiment, the camera captures a sequence of imageframes (e.g., video) when in this mode of operation. A second datumindicates the angle by which features in the image frame have beenrotated since image capture began. Again, this information can begleaned by analysis of the image data, and can be presented in textform, and/or graphically. (The graphic can comprise a circle, with aline—or arrow—through the center showing real-time angular movement ofthe camera to the left or right.)

In similar fashion, the device can track changes in the apparent size ofedges, objects, and/or other features in the image, to determine theamount by which scale has changed since image capture started. Thisindicates whether the camera has moved towards or away from the subject,and by how much. Again, the information can be presented textually andgraphically. The graphical presentation can comprise two lines: areference line, and a second, parallel line whose length changes in realtime in accordance with the scale change (larger than the reference linefor movement of the camera closer to the subject, and smaller formovement away).

Although not particularly shown in the exemplary embodiment of FIG. 38,other such geometric data can also be derived and presented, e.g.,translation, differential scaling, tip angle (i.e., forward/backward),etc.

The determinations detailed above can be simplified if the camera fieldof view includes a digital watermark having steganographiccalibration/orientation data of the sort detailed in the referencedpatent documents. However, the information can also be derived fromother features in the imagery.

Of course, in still other embodiments, data from one or more tiltsensors in the device—either alone or in conjunction with image data—canbe used to generate the presented information.

In addition to presenting such geometric information on the devicescreen, such information can also be used, e.g., in sensing gesturesmade with the device by a user, in providing context by which remotesystem responses can be customized, etc.

Camera-Based Environmental and Behavioral State Machine

In accordance with a further aspect of the present technology, a cellphone functions as a state machine, e.g., changing aspects of itsfunctioning based on image-related information previously acquired. Theimage-related information can be focused on the natural behavior of thecamera user, typical environments in which the camera is operated,innate physical characteristics of the camera itself, the structure anddynamic properties of scenes being imaged by the camera, and many othersuch categories of information. The resulting changes in the camera'sfunction can be directed toward improving image analysis programsresident on a camera-device or remotely located at some image-analysisserver. Image analysis is construed very broadly, covering a range ofanalysis from digital watermark reading, to object and facialrecognition, to 2-D and 3-D barcode reading and optical characterrecognition, all the way through scene categorization analysis and more.

A few simple examples will illustrate what is expected to become animportant aspect of future mobile devices.

Consider the problem of object recognition. Most objects have differentappearances, depending on the angle from which they are viewed. If amachine vision object-recognition algorithm is given some informationabout the perspective from which an object is viewed, it can make a moreaccurate (or faster) guess of what the object is.

People are creatures of habit, including in their use of cell phonecameras. This extends to the hand in which they typically hold thephone, and how they incline it during picture taking. After a user hasestablished a history with a phone, usage patterns may be discerned fromthe images captured. For example, the user may tend to take photos ofsubjects not straight-on, but slightly from the right. Such aright-oblique tendency in perspective may be due to the fact that theuser routinely holds the camera in the right hand, so exposures aretaken from a bit right-of-center.

(Right-obliqueness can be sensed in various ways, e.g., by lengths ofvertical parallel edges within image frames. If edges tend to be longeron the right sides of the images, this tends to indicate that the imageswere taken from a right-oblique view. Differences in illumination acrossforeground subjects can also be used—brighter illumination on the rightside of subjects suggest the right side was closer to the lens. Etc.)

Similarly, in order to comfortably operate the shutter button of thephone while holding the device, this particular user may habituallyadopt a grip of the phone that inclines the top of the camera fivedegrees towards the user (i.e., to the left). This results in thecaptured image subjects generally being skewed with an apparent rotationof five degrees to the right.

Such recurring biases can be discerned by examining a collection ofimages captured by that user with that cell phone. Once identified, datamemorializing these idiosyncrasies can be stored in a memory, and usedto optimize image recognition processes performed by the device.

Thus, the device may generate a first output (e.g., a tentative objectidentification) from a given image frame at one time, but generate asecond, different output (e.g., a different object identification) fromthe same image frame at a later time—due to intervening use of thecamera.

A characteristic pattern of the user's hand jitter may also be inferredby examination of plural images. For example, by examining pictures ofdifferent exposure periods, it may be found that the user has a jitterwith a frequency of about 4 Hertz, which is predominantly in theleft-right (horizontal) direction. Sharpening filters tailored to thatjitter behavior (and also dependent on the length of the exposure) canthen be applied to enhance the resulting imagery.

In similar fashion, through use, the device may notice that the imagescaptured by the user during weekday hours of 9:00-5:00 are routinelyilluminated with a spectrum characteristic of fluorescent lighting, towhich a rather extreme white-balancing operation needs to be applied totry and compensate. With a priori knowledge of this tendency, the devicecan expose photos captured during those hours differently than with itsbaseline exposure parameters—anticipating the fluorescent illumination,and allowing a better white balance to be achieved.

Over time the device derives information that models some aspect of theuser's customary behavior. The device then adapts some aspect of itsoperation accordingly.

The device may also adapt to its own peculiarities or degradations.These include non-uniformities in the photodiodes of the image sensor,dust on the image sensor, mars on the lens, etc.

Again, over time, the device may detect a recurring pattern, e.g.: (a)that one pixel gives a 2% lower average output signal than adjoiningpixels; (b) that a contiguous group of pixels tends to output signalsthat are about 3 digital numbers lower than averages would otherwiseindicate; (c) that a certain region of the photosensor does not seem tocapture high frequency detail—imagery in that region is consistently abit blurry, etc. From such recurring phenomena, the device can deduce,e.g., that (a) the gain for the amplifier serving this pixel is low; (b)dust or other foreign object is occluding these pixels; and (c) a lensflaw prevents light falling in this region of the photosensor from beingproperly focused, etc. Appropriate compensations can then be applied tomitigate these shortcomings.

Common aspects of the subject-matter or “scenes being imaged” is anotherrich source of information for subsequent image analysis routines, or atleast early-stage image processing steps which assist later stage imageanalysis routines by optimally filtering and/or transforming the pixeldata. For example, it may become clear over days and weeks of camerausage that a given user only uses their cameras for three basicinterests: digital watermark reading, barcode reading, and visuallogging of experimental set-ups in a laboratory. A histogram can bedeveloped over time showing which “end result” operation some givencamera usage led toward, followed by an increase in processing cyclesdevoted to early detections of both watermark and barcode basiccharacteristics. Drilling a bit deeper here, a Fourier-transformed setof image data may be preferentially routed to a quick 2-D barcodedetection function which may otherwise have been de-prioritized.Likewise on digital watermark reading, where Fourier transformed datamay be shipped to a specialized pattern recognition routine. A partiallyabstract way to view this state-machine change is that there is only afixed amount of CPU and image-processing cycles available to a cameradevice, and choices need to be made on which modes of analysis get whatportions of those cycles.

An over-simplified representation of such embodiments is shown in FIG.39.

By arrangements such as just-discussed, operation of an imager-equippeddevice evolves through its continued operation.

Focus Issues, Enhanced Print-to-Web Linking Based on Page Layout

Cameras currently provided with most cell phones, and other portablePDA-like devices, do not have adjustable focus. Rather, the optics arearranged in compromise fashion—aiming to get a decent image undertypical portrait snapshot and landscape circumstances. Imaging at closedistances generally yields inferior results—losing high frequencydetail.

The human visual system has different sensitivity to imagery atdifferent spectral frequencies. Different image frequencies conveydifferent impressions. Low frequencies give global information about animage, such as its orientation and general shape. High frequencies givefine details and edges. As shown in FIG. 52, the sensitivity of thehuman vision system peaks at frequencies of about 10 cycles/mm on theretina, and falls away steeply on either side. (Perception also dependson contrast between features sought to be distinguished—the verticalaxis.) Image features with spatial frequencies and contrast in thecross-hatched zone are usually not perceivable by humans. FIG. 53 showsan image with the low and high frequencies depicted separately (on theleft and right).

Digital watermarking of print media, such as newspapers, can be effectedby tinting the page (before, during or after printing) with aninoffensive background pattern that steganographically conveys auxiliarypayload data. Different columns of text can be encoded with differentpayload data, e.g., permitting each news story to link to a differentelectronic resource (see, e.g., U.S. Pat. Nos. 6,985,600, 6,947,571 and6,724,912).

In accordance with another aspect of the present technology, theclose-focus shortcoming of portable imaging devices is overcome byembedding a lower frequency digital watermark (e.g., with a spectralcomposition centered on the left side of FIG. 52, above the curve).Instead of encoding different watermarks in different columns, the pageis marked with a single watermark that spans the page—encoding anidentifier for that page.

When a user snaps a picture of a newspaper story of interest (whichpicture may capture just text/graphics from the desiredstory/advertisement, or may span other content as well), the watermarkof that page is decoded (either locally by the device, remotely by adifferent device, or in distributed fashion).

The decoded watermark serves to index a data structure that returnsinformation to the device, to be presented on its display screen. Thedisplay presents a map of the newspaper page layout, with differentarticles/advertisements shown in different colors.

FIGS. 54 and 55 illustrate one particular embodiment. The original pageis shown in FIG. 54. The layout map displayed on the user device screenin shown in FIG. 55.

To link to additional information about any of the stories, the usersimply touches the portion of the displayed map corresponding to thestory of interest. (If the device is not equipped with a touch screen,the map of FIG. 55 can be presented with indicia identifying thedifferent map zones, e.g., 1, 2, 3 . . . or A, B, C . . . . The user canthen operate the device's numeric or alphanumeric user interface (e.g.,keypad) to identify the article of interest.)

The user's selection is transmitted to a remote server (which may be thesame one that served the layout map data to the portable device, oranother one), which then consults with stored data to identifyinformation responsive to the user's selection. For example, if the usertouches the region in the lower right of the page map, the remote systemmay instructs a server at buick<dot>com to transmit a page forpresentation on the user device, with more information the about theBuick Lucerne. Or the remote system can send the user device a link tothat page, and the device can then load the page. Or the remote systemcan cause the user device to present a menu of options, e.g., for a newsarticle the user may be given options to: listen to a related podcast;see earlier stories on the same topic; order reprints; download thearticle as a Word file, etc. Or the remote system can send the user alink to a web page or menu page by email, so that the user can reviewsame at a later time. (A variety of such different responses touser-expressed selections can be provided, as are known from the artcited herein.)

Instead of the map of FIG. 55, the system may cause the user device todisplay a screen showing a reduced scale version of the newspaper pageitself—like that shown in FIG. 54. Again, the user can simply touch thearticle of interest to trigger an associated response.

Or instead of a presenting a graphical layout of the page, the remotesystem can return titles of all the content on the page (e.g., “BanksOwe Billions . . . ”, “McCain Pins Hopes . . . ”, “Buick Lucerne”).These titles are presented in menu form on the device screen, and theuser touches the desired item (or enters a corresponding number/letterselection).

The layout map for each printed newspaper and magazine page is typicallygenerated by the publishing company as part of its layout process, e.g.,using automated software from vendors such as Quark, Impress and Adobe,etc. Existing software thus knows what articles and advertisementsappear in what spaces on each printed page. These same software tools,or others, can be adapted to take this layout map information, associatecorresponding links or other data for each story/advertisement, andstore the resulting data structure in a web-accessible server from whichportable devices can access same.

The layout of newspaper and magazine pages offers orientationinformation that can be useful in watermark decoding. Columns arevertical. Headlines and lines of text are horizontal. Even at very lowspatial image frequencies, such shape orientation can be distinguished.A user capturing an image of a printed page may not capture the content“squarely.” However, these strong vertical and horizontal components ofthe image are readily determined by algorithmic analysis of the capturedimage data, and allow the rotation of the captured image to bediscerned. This knowledge simplifies and speeds the watermark decodingprocess (since a first step in many watermark decoding operations is todiscern the rotation of the image from its originally-encoded state).

In another embodiment, delivery of a page map to the user device from aremote server is not required. Again, a region of a page spanningseveral items of content is encoded with a single watermark payload.Again, the user captures an image including content of interest. Thewatermark identifying the page is decoded.

In this embodiment, the captured image is displayed on the devicescreen, and the user touches the content region of particular interest.The coordinates of the user's selection within the captured image dataare recorded.

FIG. 56 is illustrative. The user has used an Apple iPhone, a T-MobileAndroid phone, or the like to capture an image from an excerpt from awatermarked newspaper page, and then touches an article of interest(indicated by the oval). The location of the touch within the imageframe is known to the touch screen software, e.g., as an offset from theupper left corner, measured in pixels. (The display may have aresolution of 480×320 pixels). The touch may be at pixel position(200,160).

The watermark spans the page, and is shown in FIG. 56 by the dasheddiagonal lines. The watermark (e.g., as described in U.S. Pat. No.6,590,996) has an origin, but the origin point is not within the imageframe captured by the user. However, from the watermark, the watermarkdecoder software knows the scale of the image and its rotation. It alsoknows the offset of the captured image frame from the watermark'sorigin. Based on this information, and information about the scale atwhich the original watermark was encoded (which information can beconveyed with the watermark, accessed from a remote repository,hard-coded into the detector, etc.), the software can determine that theupper left corner of the captured image frame corresponds to a point 1.6inches below, and 2.3 inches to the right, of the top left corner of theoriginally printed page (assuming the watermark origin is at the topleft corner of the page). From the decoded scale information, thesoftware can discern that the 480 pixel width of the captured imagecorresponds to an area of the originally printed page 12 inches inwidth.

The software finally determines the position of the user's touch, as anoffset from the upper left corner of the originally-printed page. Itknows the corner of the captured image is offset (1.6″,2.3″) from theupper left corner of the printed page, and that the touch is a further5″ to the right (200 pixels×12″/480 pixels), and a further 4″ down (160pixels*12″/480pixels), for a final position within theoriginally-printed page of (6.6″,6.3″).

The device then sends these coordinates to the remote server, togetherwith the payload of the watermark (identifying the page). The serverlooks up the layout map of the identified page (from an appropriatedatabase in which it was stored by the page layout software) and, byreference to the coordinates, determines in which of thearticles/advertisements the user's touch fell. The remote system thenreturns to the user device responsive information related to theindicated article, as noted above.

Returning to focus, the close-focus handicap of PDA cameras can actuallybe turned to advantage in decoding watermarks. No watermark informationis retrieved from inked areas of text. The subtle modulations ofluminance on which most watermarks are based are lost in regions thatare printed full-black.

If the page substrate is tinted with a watermark, the useful watermarkinformation is recovered from those regions of the page that areunprinted, e.g., from “white space” between columns, between lines, atthe end of paragraphs, etc. The inked characters are “noise” that isbest ignored. The blurring of printed portions of the page introduced byfocus deficiencies of PDA cameras can be used to define amask—identifying areas that are heavily inked. Those portions may bedisregarded when decoding watermark data.

More particularly, the blurred image data can be thresholded. Any imagepixels having a value darker than a threshold value can be ignored. Putanother way, only image pixels having a value lighter than a thresholdare input to the watermark decoder. The “noise” contributed by the inkedcharacters is thus filtered-out.

In imaging devices that capture sharply-focused text, a similaradvantage may be produced by processing the text with a blurringkernel—and subtracting out those regions that are thus found to bedominated by printed text.

By arrangements such as detailed by the foregoing, deficiencies ofportable imaging devices are redressed, and enhanced print-to-weblinking based on page layout data is enabled.

Feature Extraction, Pattern Matching, Etc.

As is familiar to artisans, content-based image retrieval (CBIR)essentially involves (1) abstracting a characterization of animage—usually mathematically; and (2) using such characterizations toassess similarity between images. Two papers surveying these fields areSmeulders et al, “Content-Based Image Retrieval at the End of the EarlyYears,” IEEE Trans. Pattern Anal. Mach. Intell., Vol. 22, No. 12, pp.1349-1380, 2000, and Datta et al, “Image Retrieval: Ideas, Influencesand Trends of the New Age,” ACM Computing Surveys, Vol. 40, No. 2, April2008.

The task of identifying like-appearing imagery from large imagedatabases is a familiar operation in the issuance of drivers licenses.That is, an image captured from a new applicant is commonly checkedagainst a database of all previous driver license photos, to checkwhether the applicant has already been issued a driver's license(possibly under another name). Methods and systems known from thedriver's license field can be employed in the arrangements detailedherein.

Useful in many of the embodiments herein are image feature extractionalgorithms known as CEDD and FCTH. The former is detailed inChatzichristofis et al, “CEDD: Color and Edge Directivity Descriptor—ACompact Descriptor for Image Indexing and Retrieval,” 6th InternationalConference in advanced research on Computer Vision Systems ICVS 2008,May, 2008; the latter is detailed in Chatzichristofis et al, “FCTH:Fuzzy Color And Texture Histogram—A Low Level Feature for Accurate ImageRetrieval” 9th International Workshop on Image Analysis for MultimediaInteractive Services”, Proceedings: IEEE Computer Society, May, 2008.

Open-source software implementing these techniques is available; see theweb pagesavvash.blogspot-dot-com/2008/05/cedd-and-fcth-are-now-open-dot-html.DLLs implementing their functionality can be downloaded; the classes canbe invoked on input image data (e.g., file.jpg) as follows:

double [ ] CEDDTable = new double[144]; double [ ] FCTHTable = newdouble[144]; Bitmap ImageData = new Bitmap(“c:/file.jpg”); CEDD GetCEDD= new CEDD( ); FCTH GetFCTH = new FCTH( ); CEDDTable =GetCEDD.Apply(ImageData); FCTHTable = GetFCTH.Apply(ImageData,2);

CEDD and FCTH can be combined, to yield improved results, using theJoint Composite Descriptor file available from the just-cited web page.

Chatzichristofis has made available an open source program “img(Finder)”(see the web pagesavvash.blogspot-dot-com/2008/07/image-retrieval-in-facebook-dot-html)—acontent based image retrieval desktop application that retrieves andindexes images from the FaceBook social networking site, using CEDD andFCTH. In use, a user connects to FaceBook with their personal accountdata, and the application downloads information from the images of theuser, as well as the user's friends' image albums, to index these imagesfor retrieval with the CEDD and FCTH features. The index can thereafterbe queried by a sample image.

Chatzichristofis has also made available an online search service“img(Anaktisi)” to which a user uploads a photo, and the servicesearches one of 11 different image archives for similar images—usingimage metrics including CEDD and FCTH. Seeorpheus.ee.duth-dot-gr/anaktisi/. (The image archives include Flickr).In the associated commentary to the Anaktisi search service,Chatzichristofis explains:

-   -   “The rapid growth of digital images through the widespread        popularization of computers and the Internet makes the        development of an efficient image retrieval technique        imperative. Content-based image retrieval, known as CBIR,        extracts several features that describe the content of the        image, mapping the visual content of the images into a new space        called the feature space. The feature space values for a given        image are stored in a descriptor that can be used for retrieving        similar images. The key to a successful retrieval system is to        choose the right features that represent the images as        accurately and uniquely as possible. The features chosen have to        be discriminative and sufficient in describing the objects        present in the image. To achieve these goals, CBIR systems use        three basic types of features: color features, texture features        and shape features. It is very difficult to achieve satisfactory        retrieval results using only one of these feature types.    -   To date, many proposed retrieval techniques adopt methods in        which more than one feature type is involved. For instance,        color, texture and shape features are used in both IBM's QBIC        and MIT's Photobook. QBIC uses color histograms, a moment-based        shape feature, and a texture descriptor. Photobook uses        appearance features, texture features, and 2D shape features.        Other CBIR systems include SIMBA, CIRES, SIMPLIcity, IRMA, FIRE        and MIRROR. A cumulative body of research presents extraction        methods for these feature types.    -   In most retrieval systems that combine two or more feature        types, such as color and texture, independent vectors are used        to describe each kind of information. It is possible to achieve        very good retrieval scores by increasing the size of the        descriptors of images that have a high dimensional vector, but        this technique has several drawbacks. If the descriptor has        hundreds or even thousands of bins, it may be of no practical        use because the retrieval procedure is significantly delayed.        Also, increasing the size of the descriptor increases the        storage requirements which may have a significant penalty for        databases that contain millions of images. Many presented        methods limit the length of the descriptor to a small number of        bins, leaving the possible factor values in decimal,        non-quantized, form.    -   The Moving Picture Experts Group (MPEG) defines a standard for        content-based access to multimedia data in their MPEG-7        standard. This standard identifies a set of image descriptors        that maintain a balance between the size of the feature and the        quality of the retrieval results.    -   In this web-site a new set of feature descriptors is presented        in a retrieval system. These descriptors have been designed with        particular attention to their size and storage requirements,        keeping them as small as possible without compromising their        discriminating ability. These descriptors incorporate color and        texture information into one histogram while keeping their sizes        between 23 and 74 bytes per image.    -   High retrieval scores in content-based image retrieval systems        can be attained by adopting relevance feedback mechanisms. These        mechanisms require the user to grade the quality of the query        results by marking the retrieved images as being either relevant        or not. Then, the search engine uses this grading information in        subsequent queries to better satisfy users' needs. It is noted        that while relevance feedback mechanisms were first introduced        in the information retrieval field, they currently receive        considerable attention in the CBIR field. The vast majority of        relevance feedback techniques proposed in the literature are        based on modifying the values of the search parameters so that        they better represent the concept the user has in mind. Search        parameters are computed as a function of the relevance values        assigned by the user to all the images retrieved so far. For        instance, relevance feedback is frequently formulated in terms        of the modification of the query vector and/or in terms of        adaptive similarity metrics.    -   Also, in this web-site an Auto Relevance Feedback (ARF)        technique is introduced which is based on the proposed        descriptors. The goal of the proposed Automatic Relevance        Feedback (ARF) algorithm is to optimally readjust the initial        retrieval results based on user preferences. During this        procedure the user selects from the first round of retrieved        images one as being relevant to his/her initial retrieval        expectations. Information from these selected images is used to        alter the initial query image descriptor.”

Another open source Content Based Image Retrieval system is GIFT (GNUImage Finding Tool), produced by researchers at the University ofGeneva. One of the tools allows users to index directory treescontaining images. The GIFT server and its client (SnakeCharmer) canthen be used to search the indexed images based on image similarity. Thesystem is further described at the web pagegnu-dot-org/software/gift/gift-dot-html. The latest version of thesoftware can be found at the ftp server ftp.gnu-dot-org/gnu/gift.

Still another open source CBIR system is Fire, written by Tom Deselaersand others at RWTH Aachen University, available for download from theweb page -i6.informatik.rwth-aachen-dot-de/˜deselaers/fire/. Fire makesuse of technology described, e.g., in Deselaers et al, “Features forImage Retrieval: An Experimental Comparison”, Information Retrieval,Vol. 11, No. 2, The Netherlands, Springer, pp. 77-107, March, 2008.

Embodiments of the present invention are generally concerned withobjects depicted in imagery, rather than full frames of image pixels.Recognition of objects within imagery (sometimes termed computer vision)is a large science with which the reader is presumed to be familiar.Edges and centroids are among the image features that can be used to aidin recognizing objects in images. Shape contexts are another (c.f.,Belongie et al, Matching with Shape Contexts, IEEE Workshop on ContentBased Access of Image and Video Libraries, 2000.) Robustness to affinetransformations (e.g., scale invariance, rotation invariance) is anadvantageous feature of certain object recognition/patternmatching/computer vision techniques. (U.S. Pat. No. 6,711,293 teachesone scale-invariant set of features by which objects and features can berecognized.) Methods based on the Hough transform, and the FourierMellin transform, exhibit rotation-invariant properties.

In addition to object recognition/computer vision, the processing ofimagery contemplated in this specification (as opposed to the processingassociated metadata) can use of various other techniques, which can goby various names. Included are image analysis, pattern recognition,feature extraction, feature detection, template matching, facialrecognition, eigenvectors, etc. (All these terms are generally usedinterchangeably in this specification.) The interested reader isreferred to Wikipedia, which has an article on each of the just-listedtopics, including a tutorial and citations to related information.Excerpts from ca. September, 2008 versions of these Wikipedia articlesare appended to the end of priority applications 61/110,490, 61/103,907,and 61/100,643, and form part of the present specification.

Image metrics of the sort discussed are sometimes regarded as metadata,namely “content-dependent metadata.” This is in contrast to“content-descriptive metadata”—which is the more familiar sense in whichthe term metadata is used.

More on Image Search and Annotation

Image search functionality in certain of the foregoing embodiments canbe implemented using Pixsimilar image search software and/or the VisualSearch Developer's Kit (SDK), both from Idée, Inc. (Toronto, ON). A toolfor automatically generating descriptive annotations for imagery isALIPR (Automatic Linguistic Indexing of Pictures), as detailed in U.S.Pat. No. 7,394,947.

Other Comments

The Figures not particularly identified above show aspects ofillustrative embodiments or details of the disclosed technology.

At the end of this disclosure are listings of furtherreferences—detailing technologies and teachings that can be incorporatedinto the arrangements detailed herein (and into which the technologiesand teachings detailed herein can be incorporated).

As will be evident to the artisan, different of the actions reviewed inthis disclosure can be distributed across different hardware/softwareplatforms. For example, some may be performed on the user's cell phone,while others may be performed on one or more remote servers. Cloudcomputing can also be employed—referring tasks to services that performthem on hardware whose location and constitution may be unknown to thesystem and may change over time.

In some embodiments, the appropriate response may be determined byreference to data stored in the device—without reference to any externalresource. (The registry database used in many operating systems is oneplace where response-related data for certain inputs can be specified.)Alternatively, the information can be sent to a remote system—for it todetermine the response.

The information sent from the device may be raw pixels, or an image incompressed form, or a transformed counterpart to an image, orfeatures/metrics extracted from image data, etc. All is regarded asimage data. The receiving system can recognize the data type, or it canbe expressly identified to the receiving system (e.g., bitmap,eigenvectors, Fourier-Mellin transform data, etc.), and that system canuse the data type as one of the inputs in deciding how to process.

If the transmitted data is full image data (raw, or in a compressedform), then there will be essentially no duplication in packets receivedby processing system—essentially every picture is somewhat different.However, if the originating device performs processing on the full imageto extract features or metrics, etc., then a receiving system maysometimes receive a packet identical to one it earlier encountered (ornearly so). In this case, the response for that “snap packet” may berecalled from a cache—rather than being determined anew. (The responseinfo may be modified in accordance with user preference information, ifavailable and applicable.)

Having described and illustrated the principles of our inventive workwith reference to illustrative examples, it will be recognized that thetechnology is not so limited.

For example, while reference has been made to cell phones, it will berecognized that this technology finds utility with all manner ofdevices—both portable and fixed. PDAs, organizers, portable musicplayers, desktop computers, wearable computers, servers, etc., can allmake use of the principles detailed herein. Particularly contemplatedcell phones include the Apple iPhone, and cell phones following Google'sAndroid specification (e.g., the G1 phone, manufactured for T-Mobile byHTC Corp.). The term “cell phone” should be construed to encompass allsuch devices, even those that are not strictly-speaking cellular, nortelephones.

The design of cell phones and other computers referenced in thisdisclosure is familiar to the artisan. In general terms, each includesone or more processors, one or more memories (e.g. RAM), storage (e.g.,a disk or flash memory), a user interface (which may include, e.g., akeypad, a TFT LCD or OLED display screen, touch or other gesturesensors, a camera or other optical sensor, a microphone, etc., togetherwith software instructions for providing a graphical user interface),and an interface for communicating with other devices (which may bewireless, such as GSM, CDMA, W-CDMA, CDMA2000, TDMA, EV-DO, HSDPA, WiFi,WiMax, or Bluetooth, and/or wired, such as through an Ethernet localarea network, a T-1 internet connection, etc).

The functionality detailed above can be implemented by dedicatedhardware, or by processors executing software instructions read from amemory or storage, or by combinations thereof. References to“processors” (such as a Fourier transform processor, or an eigenfaceprocessor, etc.) should be understood to refer to functionality, ratherthan any particular form of implementation. Such processors can bededicated hardware, or software-controlled programmable hardware.Moreover, several such processors can be implemented by a singleprogrammable processor, performing multiple functions.

Software instructions for implementing the detailed functionality can bereadily authored by artisans, from the descriptions provided herein.Known artificial intelligence systems and techniques can be employed tomake the inferences, conclusions, and other determinations noted above.

Typically, the devices include operating system software that providesinterfaces to hardware devices and general purpose functions, and alsoinclude application software which can be selectively invoked to performparticular tasks desired by a user. Known browser software,communications software, and media processing software can be adaptedfor uses detailed herein. Some embodiments may be implemented asembedded systems—a special purpose computer system in which theoperating system software and the application software isindistinguishable to the user (e.g., as is commonly the case in basiccell phones). The functionality detailed in this specification can beimplemented in operating system software, application software and/or asembedded system software.

Different of the functionality can be implemented on different devices.For example, in a system in which a cell phone communicates with aserver at a remote service provider, different tasks can be performedexclusively by one device or the other, or execution can be distributedbetween the devices. Extraction of eigenvalue data from imagery is butone example of such a task. Thus, it should be understood thatdescription of an operation as being performed by a device is notlimiting but exemplary; performance of the operation by another device,or shared between devices, is also expressly contemplated. (Moreover,more than two devices may commonly be employed. E.g., a service providermay refer some tasks, such as image search, object segmentation, and/orimage classification, to servers dedicated to such tasks.)

As noted, cloud computing can be employed in the present applications.Operations need not be performed exclusively byspecifically-identifiable hardware. Rather, some operations can bereferred out to other services, which attend to their execution by stillfurther, generally anonymous, systems. Such distributed systems can belarge scale (e.g., involving computing resources around the globe), orlocal (e.g., as when a portable device identifies nearby devices throughBluetooth communication, and involves one or more of the nearby devicesin a task—such as contributing data from a local geography; see in thisregard U.S. Pat. No. 7,254,406.)

In certain embodiments it may be desirable for a capture device toinclude some form of biometric authentication, such as a fingerprintreader integrated with the shutter button, to assure than a known useris operating the device.

Some embodiments can capture several images of a subject, from differentperspectives (e.g., a video clip). Algorithms can then be applied tosynthesize a 3D model of the imaged subject matter. From such a modelnew views of the subject may be derived—views that may be more suitableas stimuli to the detailed processes (e.g., avoiding an occludingforeground object).

In embodiments using textual descriptors, it is sometimes desirable toaugment the descriptors with synonyms, hyponyms (more specific terms)and/or hypernyms (more general terms). These can be obtained from avariety of sources, including the WordNet database compiled by PrincetonUniversity.

Although many of the embodiments described above are in the context of acell phone that submits image data to a service provider, triggering acorresponding response, the technology is more generallyapplicable—whenever processing of imagery or other content occurs.

The focus of this disclosure has been on imagery. But the techniques areuseful with audio and video. The detailed technology is particularlyuseful with User Generated Content (UGC) sites, such as YouTube. Videosoften are uploaded with little or no metadata. Various techniques areapplied to identify same, with differing degrees of uncertainty (e.g.,reading watermarks; calculating fingerprints, human reviewers, etc.),and this identification metadata is stored. Further metadata isaccumulated based on profiles of users who view the video. Still furthermetadata can be harvested from later user comments posted about thevideo. (Related systems and methods in which the present technology canbe included are detailed in published patent applications 20080178302,20080165960, 20080162228, 20080059211, 20080109369, and 20080228733.) Byarrangements like that detailed herein, appropriate ad/content pairingscan be gleaned, and other enhancements to the users' experience can beoffered.

Similarly, the technology can be used with audio captured by userdevices, and recognition of captured speech. Information gleaned fromany of the captured information (e.g., OCR'd text, decoded watermarkdata, recognized speech), can be used as metadata, for the purposesdetailed herein.

Multi-media applications of this technology are also contemplated. Forexample, a user's image may be patterned-matched or GPS-matched toidentify a set of similar images in Flickr. Metadata descriptors can becollected from that set of similar images, and used to query a databasethat includes audio and/or video. Thus, a user capturing and submittingan image of a trail marker on the Appalachian Trail (FIG. 18) maytrigger download of the audio track from Aaron Copeland's “AppalachianSpring” orchestral suite to the user's cell phone, or home entertainmentsystem. (About sending content to different destinations that may beassociated with a user see, e.g., publication 20070195987.)

Watermark decoding can be used in certain embodiments. Technology forencoding/decoding watermarks is detailed, e.g., in U.S. Pat. No.6,614,914.

The present assignee has various patent filings relevant to metadata andits use, which teachings are also relevant in the presently-disclosedcontexts. See, e.g., patent publications 20070156726, 20080049971, and20070266252.

Examples of audio fingerprinting are detailed in patent publications20080276265, 20070124756, U.S. Pat. Nos. 6,990,453 and 7,359,889.Examples of image/video fingerprinting are detailed in patentpublications 20070253594 and 20020044659.

Although certain aspects of the detailed technology involved processinga large number of images to collect information, it will be recognizedthat related results can be obtained by having a large number of people(and/or automated processes) consider a single image (e.g.,crowd-sourcing). Still greater information and utility can be achievedby combining these two general approaches.

The illustrations are meant to be exemplary and not limiting. Forexample, they sometimes show multiple databases, when a single can beused (and vice-versa). Likewise, some links between the depicted blocksare not shown—for clarity's sake.

Contextual data can be used throughout the detailed embodiments tofurther enhance operation. For example, a process may depend on whetherthe originating device is a cell phone or a desktop computer; whetherthe ambient temperature is 30 or 80; the location of, and otherinformation characterizing the user; etc.

While the detailed embodiments generally presented candidateresults/actions as a series of cached displays on the cell phone screen,between which the user can rapidly switch, in other embodiments thisneed not be the case. A more traditional single-screen presentation,giving a menu of results, can be used—and the user can press a keypaddigit, or highlight a desired option, to make a selection. Or bandwidthmay increase sufficiently that the same user experience can be providedwithout locally caching or buffering data—but rather having it deliveredto the cell phone as needed.

Geographically-based database methods are detailed, e.g., in patentpublication 20030110185. Other arrangements for navigating through imagecollections, and performing search, are shown in patent publications20080010276 and 20070110338.

It is impossible to expressly catalog the myriad variations andcombinations of the technology described herein. Applicants recognizeand intend that the concepts of this specification can be combined,substituted and interchanged—both among and between themselves, as wellas with those known from the cited prior art.

To provide a comprehensive disclosure without unduly lengthening thisspecification, applicants incorporate-by-reference the patents, patentapplications, and other documents cited herein—in their entireties, aswell as the documents that they reference. (Such documents areincorporated in their entireties, even if cited above in connection withspecific of their teachings.) These references disclose technologies andteachings that can be incorporated into the arrangements detailedherein, and into which the technologies and teachings detailed hereincan be incorporated.

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We claim:
 1. A method comprising the acts: receiving a signal from abiological sensor associated with a first person, the sensor selectedfrom the group consisting of a gaze sensor, a skin conductivity sensor,an electroencephalography sensor, and a pupil dilation sensor;performing a visual object recognition operation on image informationbased, in part, on the received signal, yielding information about anobject of interest to the first person, said visual object recognitionoperation being performed by a hardware configured to perform such act;presenting information to the first user about the recognized object;determining, from data in a data archive, a second person having anassociation with the recognized object; and reporting the recognitionoperation, enabling the second person to learn thereof.
 2. The method ofclaim 1 in which the second person is a friend of the first person. 3.The method of claim 1 in which the biological sensor comprises a gazesensor.
 4. The method of claim 1 in which the object of interest is abottle of beer.
 5. The method of claim 1 in which the reportingcomprises sending information to the second person.
 6. A methodcomprising the acts: receiving a signal from a biological sensorassociated with a first person, the sensor selected from the groupconsisting of a gaze sensor, a skin conductivity sensor, anelectroencephalography sensor, and a pupil dilation sensor; performing avisual object recognition operation on image information based, in part,on the received signal, yielding information about an object of interestto the first person, said visual object recognition operation beingperformed by a hardware configured to perform such act; presentinginformation to the first user about the recognized object; and reportingrecognition of said object of interest to the first person, to a secondperson, the second person being a friend of the first person.
 7. Amethod in which operation of a camera-equipped system evolves throughits continued operation, comprising the acts: determining, from repeateduses of the camera in capturing images, a recurring operational factorincluding at least one of (a) a usage pattern behavior of a camera user,(b) an environment in which the camera is repeatedly operated, and (c)scenes that the camera repeatedly captures; applying a compensationtailored to the determined recurring operational factor, saidcompensation being applied by a hardware processor configured to performsuch act; and submitting, for analysis, image data resulting from saidcompensation; wherein the camera-equipped system changes one or moreaspects of its functioning based on image-related information previouslyacquired, so that a result of said analysis improves with continued useof the camera-equipped system.
 8. The method of claim 7 in which therecurring operational factor comprises a usage pattern behavior of thecamera user.
 9. The method of claim 8 in which the recurring operationalfactor comprises a usage pattern behavior in inclining the camera. 10.The method of claim 8 in which the recurring operational factorcomprises a usage pattern behavior in orienting the camera to captureview from a particular oblique perspective.
 11. The method of claim 8 inwhich the recurring operational factor comprises a usage patternbehavior in jittering a position of the camera.
 12. The method of claim7 in which said determining comprising discerning said recurring factorthrough examination of multiple images captured by the camera.
 13. Themethod of claim 12 in which said discerning includes discerning lengthsof parallel edges in said multiple images.
 14. The method of claim 12 inwhich said discerning includes discerning differences in illuminationacross objects in said multiple images.
 15. The method of claim 7 inwhich the act of applying a compensation to a captured image comprisesapplying a filtering operation to the captured image.
 16. The method ofclaim 7 that includes modeling an aspect of the user's customarybehavior, and adapting operation of the system accordingly.
 17. Themethod of claim 7 in which the recurring operational factor comprises anenvironment in which the camera is repeatedly operated.
 18. The methodof claim 17 that includes determining that the camera is typically usedin an environment with a particular type of lighting.
 19. The method ofclaim 18 in which the act of applying the compensation comprisescapturing imagery with an exposure tailored to said particular type oflighting.
 20. The method of claim 17 that includes determining that thecamera is typically used in an environment having a particular type oflighting, at particular times of day.
 21. The method of claim 17 thatincludes determining that the camera is typically used in an environmenthaving a particular type of lighting, at particular days of the week.22. The method of claim 7 in which the recurring operational factorcomprises scenes that the camera repeatedly captures.
 23. The method ofclaim 22 in which the recurring operational factor comprises repeatedcapture of scenes depicting barcodes.
 24. The method of claim 7 thatincludes capturing imagery, and applying said compensation after saidimagery has been captured.
 25. The method of claim 7 that includescapturing imagery, and applying said compensation as part of saidcapturing.
 26. The method of claim 7 in which said analysis includesanalyzing the image data to recognize an object depicted therein.